Distance estimation to objects and free-space boundaries in autonomous machine applications

ABSTRACT

In various examples, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors—such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that—in deployment—more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/449,310, filed Sep. 29, 2021, which is a continuation of U.S. patentapplication Ser. No. 16/813,306, filed Mar. 9, 2020 which is acontinuation-in-part of U.S. Non-Provisional application Ser. No.16/728,595, filed on Dec. 27, 2019, which claims the benefit of U.S.Provisional Application No. 62/786,188, filed on Dec. 28, 2018. Each ofthese applications is incorporated herein by reference in its entirety.

This application is related to U.S. Non-Provisional application Ser. No.16/728,598, filed on Dec. 27, 2019, U.S. Non-Provisional applicationSer. No. 16/277,895, filed on Feb. 15, 2019, and U.S. Non-Provisionalapplication Ser. No. 16/355,328, filed on Mar. 15, 2019, each of whichis hereby incorporated by reference in its entirety.

BACKGROUND

The ability to correctly detect the distance between a vehicle—such asan autonomous or semi-autonomous vehicle—and objects or obstacles in theenvironment is critical to safe operation of the vehicle. For example,accurate distance to obstacle estimates—based on sensor data—is at thecore of both longitudinal control tasks, such as automatic cruisecontrol (ACC) and automated emergency braking (AEB), and lateral controltasks, such as safety checks for lane changes as well as safe lanechange execution.

Conventional approaches to computing distance to objects or obstacles inan environment of a vehicle have relied on an assumption that a groundplane, or the Earth, is flat. Based on this assumption,three-dimensional (3D) information may be modeled using two-dimensional(2D) information sources—such as a 2D image. For example, because theground plane is assumed to be flat, conventional systems further assumethat the bottom of a two-dimensional bounding box corresponding to adetected object is located on the ground plane. As such, once an objectis detected, and based on this flat ground assumption, simple geometryis used to calculate the distance of the given object or obstacle fromvehicle.

However, these conventional approaches suffer when the actual roadsurfaces defining the actual ground plane are curved or otherwise notflat. For example, when applying the assumption that the ground plane isflat when in fact it is not, a curve in a driving surface causesinaccurate predictions—e.g., over- or under-estimated—with respect todistances to objects or obstacles in the environment. In eitherscenario, inaccurate distance estimates have a direct negativeconsequence on various operations of the vehicle, thereby potentiallycompromising the safety, performance, and reliability of both lateraland longitudinal control or warning related driving features. As anexample, an under-estimated distance may result in failure to engage ACCand, even more critically, failure to engage AEB features to prevent apotential collision. Conversely, an over-estimated distance may resultin failure of ACC or AEB features being activated when not needed,thereby causing potential discomfort or harm to passengers, while alsolowering confidence of the passengers with respect to the ability of thevehicle to perform safely.

Another drawback of conventional systems is the reliance on generatingground truth data at an output resolution of a deep neural network (DNN)in order to accurately train the DNN. For example, in conventionalsystems, data used for ground truth generation may captured at an inputresolution and may be rasterized at the output resolution—which may bemore or less than the input resolution. This is not a trivial task, andoften results in inaccurate ground truth generation that includesartifacts—thereby resulting in a DNN that is not as accurate asdesirable for safety critical applications, such as autonomous orsemi-autonomous driving.

SUMMARY

Embodiments of the present disclosure relate to distance to obstacle,object, and/or free-space boundary computations in autonomous machineapplications. Systems and methods are disclosed that accurately androbustly predict distances to objects, obstacles, a free-space boundary,and/or other portions of an environment using a deep neural network(DNN) trained with sensor data—such as LIDAR data, RADAR data, SONARdata, image data, and/or the like—and/or free-space boundary informationgenerated by one or more DNNs, object detection algorithms, and/orcomputer vision algorithms. For example, by using sensor data, futuremotion of the ego-vehicle, and/or free-space boundary information fortraining the DNN, the predictions of the DNN in deployment—when usingimage data alone, in embodiments—are accurate and reliable even fordriving surfaces that are curved or otherwise not flat.

In contrast to conventional systems, such as those described above, aDNN may be trained—using one or more sensors, such as LIDAR sensors,RADAR sensors, SONAR sensors, vehicle sensors (e.g., speed sensors,location sensors, etc.), and/or the like, in addition to free-spaceboundary information—to predict distances to objects, obstacles, and/ora free-space boundary in the environment using image data generated byone or more cameras of a vehicle. As such, by leveraging depth sensorsand/or motion of the ego-vehicle for ground truth generation duringtraining, the DNN may accurately predict—in deployment—distances toobjects, obstacles, and/or a free-space boundary in the environmentusing image data alone. In addition, because embodiments are not limitedto a flat ground estimation—a drawback of conventional systems—the DNNmay be able to robustly predict distances that correspond to an actualtopology of the driving surface.

The ground truth data encoding pipeline may use sensor data fromsensor(s) of an ego-vehicle to—automatically, without manual annotation,in embodiments—encode ground truth data corresponding to training imagedata in order to train the DNN to make accurate predictions from imagedata alone. As a result, training bottlenecks that result from manuallabeling may be removed, and the training period may be reduced. Inaddition, in some embodiments, a camera adaptation algorithm may be usedto overcome the variance in intrinsic characteristics across cameramodels, thereby allowing the DNN to perform accurately, irrespective ofthe camera model.

In some embodiments, to avoid the requirement of rasterizing at outputresolutions—a challenging task of conventional systems—a samplingalgorithm may be used to sample depth values from a predicted depth mapat an output resolution of the DNN, and extrapolate those values todistance values corresponding to ground truth information at an inputresolution of the DNN. As a result, a loss function(s) may use sensordata at the input resolution—after sampling—to determine the accuracy ofthe predictions of the DNN and to update or tune parameters of the DNNto a desired accuracy for deployment.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for distance estimation to obstacles,objects, and/or free-space boundaries in autonomous machine applicationsare described in detail below with reference to the attached drawingfigures, wherein:

FIG. 1 is a data flow diagram for a process of training a machinelearning model(s) to predict distances to objects and/or obstacles in anenvironment, in accordance with some embodiments of the presentdisclosure;

FIG. 2 is a data flow diagram for ground truth data encoding usingsensor data, in accordance with some embodiments of the presentdisclosure;

FIG. 3A is a visualization of ground truth data generated by a LIDARsensor(s), in accordance with some embodiments of the presentdisclosure;

FIG. 3B is a visualization of ground truth data generated by a RADARsensor(s), in accordance with some embodiments of the presentdisclosure;

FIG. 4 is an illustration of various calculations used in a cameraadaptation algorithm, in accordance with some embodiments of the presentdisclosure;

FIG. 5A includes visualizations of ground truth masks and depth mappredictions of a machine learning model(s) based on varying sensorparameters, in accordance with some embodiments of the presentdisclosure;

FIG. 5B includes illustrations of distortion maps and histograms forsensors having varying parameters, in accordance with some embodimentsof the present disclosure;

FIG. 6 is a flow diagram showing a method for training a machinelearning model(s) to predict distances to objects and/or obstacles in anenvironment, in accordance with some embodiments of the presentdisclosure;

FIG. 7 is a data flow diagram for a process of predicting distances toobjects and/or obstacles in an environment using a machine learningmodel(s), in accordance with some embodiments of the present disclosure;

FIGS. 8A-8B are visualizations of object detections and depthpredictions based on outputs of a machine learning model(s), inaccordance with some embodiments of the present disclosure;

FIG. 9 is a flow diagram showing a method for predicting distances toobjects and/or obstacles in an environment using a machine learningmodel(s), in accordance with some embodiments of the present disclosure;

FIG. 10A is a chart illustrating a calculation of safety bounds forclamping distance predictions of a machine learning model(s), inaccordance with some embodiments of the present disclosure;

FIG. 10B is a chart illustrating a maximum upward contour for safetybounds computations, in accordance with some embodiments of the presentdisclosure;

FIG. 10C is an illustration of calculating an upper safety bounds, inaccordance with some embodiments of the present disclosure;

FIG. 10D is a chart illustrating a maximum downward contour for safetybounds computations, in accordance with some embodiments of the presentdisclosure;

FIG. 10E is an illustration of calculating a lower safety bounds, inaccordance with some embodiments of the present disclosure;

FIG. 10F is an illustration of a safety band profile, in accordance withsome embodiments of the present disclosure;

FIG. 11 is a flow diagram showing a method for safety boundsdeterminations using road shape, in accordance with some embodiments ofthe present disclosure;

FIG. 12 is an illustration of calculating safety bounds using a boundingshape corresponding to an object, in accordance with some embodiments ofthe present disclosure;

FIG. 13 a flow diagram showing a method for safety bounds determinationsusing bounding shape properties, in accordance with some embodiments ofthe present disclosure;

FIG. 14 is a data flow diagram for a process of training a machinelearning model(s) to predict distances to objects, obstacles, and/or afree-space boundary in an environment, in accordance with someembodiments of the present disclosure;

FIG. 15A is a visualization of free-space boundary depth estimationusing future motion of an ego-vehicle, in accordance with someembodiments of the present disclosure;

FIG. 15B is an illustration of an example image captured by a camera ofan ego-vehicle, in accordance with some embodiments of the presentdisclosure;

FIG. 15C is a visualization of a ground truth depth map along afree-space boundary corresponding to the image of FIG. 15B, inaccordance with some embodiments of the present disclosure;

FIG. 16A is a visualization of LIDAR data used for generating groundtruth data for training a machine learning model(s), in accordance withsome embodiments of the present disclosure;

FIG. 16B is a visualization of filtered LIDAR data used for generatingground truth data for training a machine learning model(s), inaccordance with some embodiments of the present disclosure;

FIG. 16C is an illustration of an example image captured by a camera ofan ego-vehicle, in accordance with some embodiments of the presentdisclosure;

FIG. 16D is a visualization of a ground truth depth map corresponding tothe image of FIG. 16C, in accordance with some embodiments of thepresent disclosure;

FIG. 17 is an example illustration of sampling depth values from apredicted depth map for training a machine learning model(s), inaccordance with some embodiments of the present disclosure;

FIG. 18 is a flow diagram showing a method for training a machinelearning model(s) to predict distances to obstacles, objects, and/or adetected free-space boundary in an environment, in accordance with someembodiments of the present disclosure;

FIG. 19 is a flow diagram showing a method sampling depth values from apredicted depth map for training a machine learning model(s), inaccordance with some embodiments of the present disclosure;

FIG. 20 is a flow diagram showing a method for predicting—indeployment—distance to obstacles, objects, and/or a detected free-spaceboundary in an environment, in accordance with some embodiments of thepresent disclosure;

FIG. 21A is an illustration of an example autonomous vehicle, inaccordance with some embodiments of the present disclosure;

FIG. 21B is an example of camera locations and fields of view for theexample autonomous vehicle of FIG. 21A, in accordance with someembodiments of the present disclosure;

FIG. 21C is a block diagram of an example system architecture for theexample autonomous vehicle of FIG. 21A, in accordance with someembodiments of the present disclosure;

FIG. 21D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle of FIG. 21A, in accordancewith some embodiments of the present disclosure; and

FIG. 22 is a block diagram of an example computing device suitable foruse in implementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

Systems and methods are disclosed related to computing distances toobstacles, objects, a free-space boundary(ies), and/or other portions ofan environment using one or more machine learning model(s), and systemand methods for training the machine learning model(s) to accurately androbustly predict the same. Although the present disclosure may bedescribed with respect to an example autonomous vehicle 2100(alternatively referred to herein as “vehicle 2100”, “ego-vehicle 2100”,or “autonomous vehicle 2100,” an example of which is described withrespect to FIGS. 21A-21D, this is not intended to be limiting. Forexample, the systems and methods described herein may be used by,without limitation, non-autonomous vehicles, semi-autonomous vehicles(e.g., in one or more adaptive driver assistance systems (ADAS)),robots, warehouse vehicles, off-road vehicles, flying vessels, boats,shuttles, emergency response vehicles, motorcycles, electric ormotorized bicycles, aircraft, construction vehicles, underwater craft,drones, and/or other vehicle types. In addition, although the presentdisclosure may be described with respect to autonomous driving or ADASsystems, this is not intended to be limiting. For example, the systemsand methods described herein may be used in simulation environment(e.g., to test accuracy of machine learning models during simulation),in robotics, aerial systems, boating systems, and/or other technologyareas, such as for perception, world model management, path planning,obstacle avoidance, and/or other processes.

Training a Machine Learning Model(s) for Distance to Object Predictions

Now referring to FIG. 1 , FIG. 1 is a data flow diagram for a process100 of training a machine learning model(s) to predict distances toobjects and/or obstacles in an environment, in accordance with someembodiments of the present disclosure. The process 100 may includegenerating and/or receiving sensor data 102 from one or more sensors ofthe vehicle 2100. In deployment, the sensor data 102 may be used by thevehicle 2100, and within the process 100, to predict depths and/ordistances to one or more objects or obstacles—such as other vehicles,pedestrians, static objects, etc. —in the environment. For example, thedistances predicted may represent a value in a “z” direction, which maybe referred to as a depth direction. The sensor data 102 may include,without limitation, sensor data 102 from any of the sensors of thevehicle 2100 (and/or other vehicles or objects, such as robotic devices,VR systems, AR systems, etc., in some examples). For example, and withreference to FIGS. 21A-21C, the sensor data 102 may include the datagenerated by, without limitation, global navigation satellite systems(GNSS) sensor(s) 2158 (e.g., Global Positioning System sensor(s)), RADARsensor(s) 2160, ultrasonic sensor(s) 2162, LIDAR sensor(s) 2164,inertial measurement unit (IMU) sensor(s) 2166 (e.g., accelerometer(s),gyroscope(s), magnetic compass(es), magnetometer(s), etc.),microphone(s) 2196, stereo camera(s) 2168, wide-view camera(s) 2170(e.g., fisheye cameras), infrared camera(s) 2172, surround camera(s)2174 (e.g., 360 degree cameras), long-range and/or mid-range camera(s)2198, speed sensor(s) 2144 (e.g., for measuring the speed of the vehicle2100), and/or other sensor types. Although reference is primarily madeto the sensor data 102 corresponding to LIDAR data, RADAR data, andimage data, this is not intended to be limiting, and the sensor data 102may alternatively or additionally be generated by any of the sensors ofthe vehicle 2100, another vehicle, and/or another system (e.g., avirtual vehicle in a simulated environment).

In some examples, the sensor data 102 may include the sensor datagenerated by one or more forward-facing sensors, side-view sensors,and/or rear-view sensors. This sensor data 102 may be useful foridentifying, detecting, classifying, and/or tracking movement of objectsaround the vehicle 2100 within the environment. In embodiments, anynumber of sensors may be used to incorporate multiple fields of view(e.g., the fields of view of the long-range cameras 2198, theforward-facing stereo camera 2168, and/or the forward facing wide-viewcamera 2170 of FIG. 21B) and/or sensory fields (e.g., of a LIDAR sensor2164, a RADAR sensor 2160, etc.).

In some embodiments, a machine learning model(s) 104 may be trained topredict object distance(s) 106 and/or object detection(s) 116 usingimage data alone. For example, the process 100 may be used to train themachine learning model(s) 104 to predict the object distance(s) 106—or adepth map that may be converted to distances—of one or more objectsand/or obstacles in the environment using images alone as input data. Inaddition, in some embodiments, the machine learning model(s) 104 may betrained to intrinsically compute the object detection(s) 116 (however,in some embodiments, the object detection(s) may be computed by anobject detector—such as an object detection algorithm, a computer visionalgorithm, a neural network, etc.). In order to more effectively trainthe machine learning model(s) 104, however, additional data from thesensor data 102—such as LIDAR data, RADAR data, SONAR data, and/or thelike—may be used to generate ground truth data corresponding to theimages (e.g., via ground truth encoding 110). In return, the groundtruth data may be used to increase the accuracy of the machine learningmodel(s) 104 at predicting the object distance(s) 106 and/or the objectdetection(s) 116 by leveraging the additional accuracy of thissupplemental sensor data 102 (e.g., LIDAR data, RADAR data, SONAR data,etc.).

With respect to the inputs to the machine learning model(s) 104, thesensor data 102 may include image data representing an image(s) and/orimage data representing a video (e.g., snapshots of video). Where thesensor data 102 includes image data, any type of image data format maybe used, such as, for example and without limitation, compressed imagessuch as in Joint Photographic Experts Group (JPEG) orLuminance/Chrominance (YUV) formats, compressed images as framesstemming from a compressed video format such as H.264/Advanced VideoCoding (AVC) or H.265/High Efficiency Video Coding (HEVC), raw imagessuch as originating from Red Clear Blue (RCCB), Red Clear (RCCC), orother type of imaging sensor, and/or other formats. In addition, in someexamples, the sensor data 102 may be used within the process 100 withoutany pre-processing (e.g., in a raw or captured format), while in otherexamples, the sensor data 102 may undergo pre-processing (e.g., noisebalancing, demosaicing, scaling, cropping, augmentation, whitebalancing, tone curve adjustment, etc., such as using a sensor datapre-processor (not shown). As used herein, the sensor data 102 mayreference unprocessed sensor data, pre-processed sensor data, or acombination thereof.

As a non-limiting embodiment, to generate the ground truth data fortraining the machine learning model(s) 104, ground truth encoding 110may be performed according to the process for ground truth encoding 110of FIG. 2 . For example, the sensor data 102—such as image datarepresentative of one or more images—may be used by an object detector214 to detect objects and/or obstacles represented by the image data.For example, persons, animals, vehicles, signs, poles, traffic lights,buildings, flying vessels, boats, and/or other types of objects and/orobstacles may be detected by the object detector 214. An output of theobject detector 214 may be locations of bounding shapes (e.g., boundingboxes, rectangles, squares, circles, polygons, etc.) corresponding tothe objects or obstacles represented by the image data. Once thebounding shape locations and dimensions are known with respect to aparticular image, additional sensor data 102—such as LIDAR data and/orRADAR data, as non-limiting examples—may be used to determine distancesto the objects or obstacles corresponding to the respective boundingshapes. As such, where a distance to an object or obstacle may bedifficult to ascertain accurately using image data alone—or anothertwo-dimensional representation—this additional sensor data 102 may beused to increase the accuracy of the predictions with respect to thedistances to objects or obstacles within the images.

In some embodiments, the ground truth encoding 110 may occurautomatically without manual and/or human labeling or annotations. Forexample, because conversions from world-space outputs of one or moreLIDAR sensors, RADAR sensors, SONAR sensors, etc. to image-space outputsof one or more cameras may be known, and because the locations anddimensions of bounding shapes within the image-space may be known, thedistances (e.g., LIDAR distances 216, RADAR distances 218, etc.) may becorrelated automatically with the objects and/or obstacles within theimages. As an example, where a distance(s) to one or more objects inworld-space is determined to correspond to a region of image-spaceassociated with a bounding shape of an object, the distance(s) may beassociated with the object for the purposes of ground truth encoding110. In some examples, a single distance value may be correlated to eachobject while in other examples the distances corresponding to an objectmay vary based on varying distance outputs of LIDAR sensors, RADARsensors, SONAR sensors, and/or the like.

In some embodiments, LIDAR distance(s) 216 determined from LIDAR datagenerated by one or more LIDAR sensor(s) 2164 may be used for groundtruth encoding 110 of distances. For example, and with respect to FIG.3A, bounding shapes 304A-304E corresponding respectively to objects306A-306E may be generated by the object detector 214 and associatedwith an image 302. In addition, LIDAR data—represented by LIDAR points310 in the visualization of FIG. 3A—may be associated with the image302. For example, as described herein, conversions between world-spacelocations and corresponding image-space locations of LIDAR data may beknown, or determined, using intrinsic and/or extrinsic parameters—e.g.,after calibration—of the LIDAR sensor(s) 2165 and/or the camera(s) thatgenerated the image 302. As such, because this relationship betweenworld-space and image-space is known, and because the LIDAR data and theimage data may have been captured substantially simultaneously, theLIDAR data distance predictions may be associated with the variousobjects 306—or their corresponding bounding shapes 304—in the image 302.

Although the LIDAR points are only illustrated within the boundingshapes 304, this is not intended to be limiting and is for illustrativepurposes only. In some examples, the LIDAR points may be generated tocorrespond to the entire image 302, or to correspond to additional oralternative portions of the image 302 than the visualization of FIG. 3Aillustrates.

In some embodiments, a cropped bounding shape 308 may be generated foreach object 306 that is equal to or lesser in size than the boundingshape 304 corresponding to the object. For example, because the boundingshapes 304 output by an object detector (e.g., an object detectionneural network, a computer vision algorithm, or another object detectionalgorithm) may include additional portions of the environment that arenot part of the object 306 or obstacle. As such, and in an effort toincrease accuracy of the reconciliation of the depth values from theLIDAR data with pixels of the image 302 that correspond to the object306 or obstacle, the cropped bounding shapes 308 may be created withinthe bounding shapes 304.

In some examples, the dimensions of the cropped bounding shapes 308 maybe determined based on a distance of the object 306 from a referencelocation (e.g., from the ego-vehicle, from the camera, from the LIDARsensor, etc.), such that as an object moves further away from areference location, the amount of cropping changes. For example, theamount, percentage (e.g., percentage of the bounding shape 304), orratio (ratio of the size of the cropped bounding shape 308 with respectto the bounding shape 304, or vice versa) of cropping may decrease asthe distance of the object 306 increases, or vice versa. In suchexamples, there may be a calculated change in the amount, percentage, orratio of cropping according to distance (e.g., using one or moreequations, curves, relationships, functions, etc.), or there may bezones, where particular distance zones correspond to a certain amount,percentage, or ratio of cropping. For instance, at a first range ofdistances from 0-10 meters, the crop may be 50%, at 10-20 meters, thecrop may be 40%, at 20-40 meters, the crop may be 35%, and so on.

In some embodiments, the dimensions of the cropped bounding shapes 308may be determined differently for different sides or edges of thecropped bounding shapes 308. For example, a bottom crop of the boundingshape 304 to generate a corresponding cropped bounding shape 308 may bea different amount, percentage, or ratio than a top crop, a left sidecrop, and/or a right side crop, a top crop of the bounding shape 304 togenerate a corresponding cropped bounding shape 308 may be a differentamount, percentage, or ratio than a bottom crop, a left side crop,and/or a right side crop, and so on. For example, a bottom crop may be aset amount, percentage, or ratio for each cropped bounding shape 308while the top crop may change based on some factor or variable—such asdistance from the reference location, type of object, etc. —or viceversa. As a non-limiting example, the bottom crop may always be 10%,while the top crop may be in a range between 10% and 20%, where a valuewithin the range is determined based on some function of distance of theobject 306 from a reference location.

In at least one embodiment, the LIDAR points 310 used to determine thedistance of an object 306 may be the LIDAR points 310 that correspond tothe cropped bounding shape 308. As a result, in such embodiments, thelikelihood that the depths or distances determined to correspond to theobject 306 actually correspond to the object 306 is increased. In otherembodiments, the LIDAR points 310 used to determine the distance to anobject may be the LIDAR points 310 that correspond to the boundingshapes 304 (and the cropped bounding shapes 304 may not be used, orgenerated, in such embodiments). The distance that is associated witheach object 306 (e.g., 10.21 meters (m) for the object 306A, 14.90 m forthe object 306B, 24.13 m for the object 306C, 54.45 m for the object306D, and 58.86 m for the object 306E) may be determined using one ormore of the LIDAR points 310 associated with the corresponding boundingshape 304 and/or cropped bounding shape 308. For example, distancesassociated with each of the LIDAR points 310 within the bounding shape304 and/or the bounding shape 308 may be averaged to generate the finaldistance value. As another example, a LIDAR point 310 closest to acentroid of the bounding shape 304 and/or the cropped bounding shape 308may be used to determine the final distance value. In a further example,a group or subset of the LIDAR points 310—such as a subset within aregion near a centroid of the bounding shape 304 and/or the croppedbounding shape 308—may be used to determine the final distance value foran object 306 (e.g., by averaging, weighting, and/or otherwise using thedistance values associated with each of the group or subset of the LIDARpoints 310 to compute the final distance value).

In addition, in some embodiments, to help reduce noise in the LIDARpoints 310 projected into the image-space, a filtering algorithm may beapplied to remove or filter out noisy LIDAR points 310. For example, andwithout limitation, a random sample consensus (RANSAC) algorithm may beapplied to the camera-to-LIDAR data point associations to cluster andfilter out the noisy LIDAR points 310. As a result of using a filteringalgorithm, such as RANSAC, the surviving LIDAR points 310 that arewithin a given bounding shape 304 and/or cropped bounding shape 308 maybe interpreted to be a common distance away from the camera or otherreference location.

In some embodiments, RADAR distance(s) 218 determined from RADAR datagenerated by one or more RADAR sensor(s) 2160 may be used for groundtruth encoding 110 of distances. For example, and with respect to FIG.3B, bounding shapes 304A-304E corresponding respectively to objects306A-306E may be generated by the object detector 214 and associatedwith an image 302. In addition, RADAR data—represented by RADAR points312 in the visualization of FIG. 3B—may be associated with the image302. For example, as described herein, conversions between world-spacelocations and corresponding image-space locations of RADAR data may beknown, or determined, using intrinsic and/or extrinsic parameters—e.g.,after calibration—of the RADAR sensor(s) 2160 and/or the camera(s) thatgenerated the image 302. In some embodiments, RADAR target clusteringand tracking may be used to determine the associations between RADARpoints 312 and objects 306—or bounding shapes 304 corresponding thereto.As such, because this relationship between world-space and image-spaceis known, and because the RADAR data and the image data may have beencaptured substantially simultaneously, the RADAR data distancepredictions may be associated with the various objects 306—or theircorresponding bounding shapes 304—in the image 302.

Although the RADAR points 312 are only illustrated within the boundingshapes 304, this is not intended to be limiting and is for illustrativepurposes only. In some examples, the RADAR points may be generated tocorrespond to the entire image 302, or to correspond to additional oralternative portions of the image 302 than the visualization of FIG. 3Billustrates.

In some embodiments, similar to the description herein with respect tothe FIG. 3A, a cropped bounding shape 308 (not illustrated in FIG. 3B)may be generated for each object 306 that is equal to or lesser in sizethan the bounding shape 304 corresponding to the object. In suchembodiments, and in an effort to increase accuracy of the reconciliationof the depth values from the RADAR data with pixels of the image 302that correspond to the object 306 or obstacle, the cropped boundingshapes 308 may be created within the bounding shapes 304. As such, in atleast one embodiment, the RADAR points 312 used to determine thedistance of an object 306 may be the RADAR points 312 that correspond tothe cropped bounding shape 308.

The distance that is associated with each object 306 (e.g., 13.1 m forthe object 306A, 18.9 m for the object 306B, 28.0 m for the object 306C,63.3 m for the object 306D, and 58.6 m for the object 306E) may bedetermined using one or more of the RADAR points 312 associated with thecorresponding bounding shape 304 and/or cropped bounding shape 308. Forexample, distances associated with each of the RADAR points 312 withinthe bounding shape 304 (e.g., the RADAR points 312A and 312B in FIG. 3B)and/or the bounding shape 308 may be averaged to generate the finaldistance value. As another example, a single RADAR point 312 may beselected for use in computing the final distance value. For example, asillustrated in FIG. 3B, the RADAR point 312A may be used for the object306A (as indicated by the cross-hatching) while the RADAR point 312B maynot be used. For example, a confidence may be associated with thecamera-to-RADAR points such that a higher confidence point may beselected (e.g., the RADAR point 312A may be selected over the RADARpoint 312B). The confidence may be determined using any calculation,such as, without limitation, a distance to a centroid of the boundingshape 304 and/or the cropped bounding shape 308.

Once the final distance values have been determined for each object 306using the LIDAR data and/or the RADAR data (and/or SONAR data,ultrasonic data, etc.), a determination may be made as to which of thefinal distance values should be used for each object 306 may be made.For example, for each object 306, a determination as to whether theLIDAR distance(s) 216, the RADAR distance(s) 218, and/or a combinationthereof should be used for generating a ground truth depth map 222 maybe made. Where a distance for a particular object 306 has only beencomputed from one depth sensor modality (e.g., RADAR or LIDAR), thedistance associated with the object 306 may be the distance from the onedepth sensor modality. Where two or more modalities have computeddistances for a particular object 306, a noisiness threshold 220 may beused to determine which modality(ies) to use for the distance values. Insome non-limiting embodiments, the noisiness threshold 220 may beoptimized as a hyper-parameter. Although any number of depth sensormodalities may be used, in examples using RADAR and LIDAR, a singlemodality may be selected over the other where both have correspondingdepth values for an object. For example, LIDAR distance(s) 216 may beselected over RADAR distance(s) 218, or vice versa. In other examples,one modality may be selected below a threshold distance and another maybe selected beyond the threshold distance. In such examples, the LIDARdistance(s) 216 may be used at closer distances (e.g., within 40 metersof the camera or other reference location), and RADAR distance(s) 218may be used at further distances (e.g., further than 40 meters from thecamera or other reference location). Using a threshold distance in thisway may leverage the accuracy of various depth sensor modalities overvarying distance ranges. In at least one embodiment, the LIDARdistance(s) 216 and the RADAR distance(s) 218, where both are computedfor an object 306, may be averaged or weighted to compute a singlecombined distance value. For example, the two distances may be averagedwith equal weight, or one modality may be weighted greater than theother. Where weighting is used, the determination of the weight for eachmodality may be constant (e.g., 60% for LIDAR and 40% for RADAR) or mayvary depending on some factor, such as distance (e.g., within 50 metersof the camera or other reference location, LIDAR is weighted 70% andRADAR is weighted 30%, while beyond 50 meters of the camera or otherreference location, LIDAR is weighted 40% and RADAR is weighted 60%). Assuch, the determination of which distance value should be the finaldistance value for a particular object 306 may be made using one or moredepth sensor modalities and may depend on a variety of factors (e.g.,availability of data from various depth sensor modalities, distance ofan object from the reference location, noisiness of the data, etc.).

In some examples, the LIDAR distance(s) 216 and/or the RADAR distance(s)218 may be further enhanced by applying a time-domain stateestimator—based on a motion model—on object tracks. Using this approach,noisy readings from LIDAR and/or RADAR may be filtered out. A stateestimator may further model covariance of the state, which may representa measure of uncertainty on the ground truth depth value. Such a measuremay be utilized in training and evaluation of the machine learningmodel(s) 104, for instance, by down-weighting loss for high uncertaintysamples.

Once a final distance value(s) has been selected for an object 306, oneor more pixels of the image 302 may be encoded with the final depthvalue(s) to generate the ground truth depth map 222. In somenon-limiting embodiments, to determine the one or more pixels to beencoded for the object 306, each of the pixels associated with thebounding shape 304 and/or the cropped bounding shape 308 may be encodedwith the final distance value(s). However, in such examples, where twoor more bounding shapes 304 and/or cropped bounding shapes 308 at leastpartially overlap (e.g., one bounding shape 304 occludes another), usingeach of the pixels of the bounding shape 304 and/or the cropped boundingshape 308 may result in one or more of the objects 306 not beingrepresented sufficiently in the ground truth depth map 222. As such, insome embodiments, a shape—such as a circle or ellipse—may be generatedfor each object. The shape, in some examples, may be centered at acentroid of the bounding shape 304 and/or the cropped bounding shape308. By generating a circle or ellipse, the potential for occlusionleading to lack of representation of an object 306 in the ground truthdepth map 222 may be reduced, thereby increasing the likelihood thateach of the objects 306 are represented in the ground truth depth map222. As a result, the ground truth depth map 222 may represent theground truth distance(s) encoded onto an image—e.g., a depth map image.The ground truth depth map 222—or depth map image—may then be used asground truth for training the machine learning model(s) 104 to predictdistances to objects using images generated by one or more cameras. Assuch, the machine learning model(s) 104 may be trained to predict—indeployment—a depth map corresponding to the objects and/or obstaclesdepicted in images captured by the vehicle 2100 (and/or another vehicletype, a robot, a simulated vehicle, a water vessel, an aircraft, adrone, etc.).

Ground truth encoding 110 with respect to the predictions of the objectdetection(s) 116 may include labeling, or annotating, the sensor data102 (e.g., images, depth maps, point clouds, etc.) with bounding shapesand/or corresponding class labels (e.g., vehicle, pedestrian, building,airplane, watercraft, street sign, etc.). As such, the ground truthannotations or labels may be compared, using loss function(s) 108, tothe predictions of the object detection(s) 116 by the machine learningmodel(s) 104 to update and optimize the machine learning model(s) 104for predicting locations of objects and/or obstacles.

With respect to automatically (e.g., for encoding the ground truth depthmap 222) and/or manually generating ground truth annotations, theannotations for the training images may be generated within a drawingprogram (e.g., an annotation program), a computer aided design (CAD)program, a labeling program, another type of program suitable forgenerating the annotations, and/or may be hand drawn, in some examples.In any example, the annotations may be synthetically produced (e.g.,generated from computer models or renderings), real produced (e.g.,designed and produced from real-world data), machine-automated (e.g.,using feature analysis and learning to extract features from data andthen generate labels), human annotated (e.g., labeler, or annotationexpert, defines the location of the labels), and/or a combinationthereof (e.g., human formulates one or more rules or labelingconventions, machine generates annotations). In some examples, the LIDARdata, RADAR data, image data, and/or other sensor data 102 that is usedas input to the machine learning model(s) 104 and/or used to generatethe ground truth data may be generated in a virtual or simulatedenvironment. For example, with respect to a virtual vehicle (e.g., acar, a truck, a water vessel, a construction vehicle, an aircraft, adrone, etc.), the virtual vehicle may include virtual sensors (e.g.,virtual cameras, virtual LIDAR, virtual RADAR, virtual SONAR, etc.) thatcapture simulated or virtual data of the virtual or simulatedenvironment. As such, in some embodiments, in addition to oralternatively from real-world data being used as inputs to the machinelearning model(s) 104 and/or for ground truth generation, simulated orvirtual sensor data may be used and thus included in the sensor data102.

Referring again to FIG. 1 , camera adaptation 112 may be performed insome embodiments in an effort to make the machine learning model(s) 104invariant to underlying camera intrinsic characteristics. For example,to account for the underlying challenge of similar objects that are asame distance from a reference location—e.g., a camera—appearingdifferently depending on camera parameters (e.g., field of view), acamera adaptation algorithm may be used to enable camera intrinsicinvariance. Were the variance in camera intrinsic parameters notaccounted for, the performance of the machine learning model(s) 104 maybe degraded for distance to object or obstacle estimation solutions.

In some embodiments, camera adaptation 112 may include applying ascaling factor to camera-based image labels. As a non-limiting example,if a camera with a 60 degree field of view is used as a referencecamera, a multiplier of 2× may be applied to labels of images of exactlythe same scene produced by a camera with a 120 degree field of view,because the objects produced by this camera will look half the sizecompared to those generated by the reference camera. Similarly, asanother non-limiting example, if the same reference camera is used, amultiplier of negative 2× may be applied to labels of images of exactlythe same scene produced by a camera with a 30 degree field of view,because the objects produced by this camera will look twice the sizecompared to those generated by the reference camera.

In at least on embodiment, camera adaptation 112 includes generatingscaling and distortion maps as an extra input to the machine learningmodel(s) 104. This may allow the camera model information to beavailable as an input—as indicated by the dashed line arrow from cameraadaptation 112 to the machine learning model(s) 104 in FIG. 1 —forlearning. For example, with reference to FIG. 4 , FIG. 4 is anillustration 400 of various calculations used in a camera adaptationalgorithm, in accordance with some embodiments of the presentdisclosure. With respect to the illustration 400, x (e.g., the x-axis),y (e.g., the y-axis), and z (e.g., the z-axis) represent 3D coordinatesof locations in the camera coordinate system, while u (e.g., the u-axis)and v (e.g., the v-axis) represent 2D coordinates in the camera imageplane. A position, p, denotes a 2-vector [u, v] as a position in theimage plane. A principal point, at u_(o), v_(o), represents where thez-axis intersects the image plane. θ, ϕ, d represent another 3D location(x, y, z) where d is the depth (e.g., a position along the z-axis), θ isthe angle between the z-axis and the vector [x, y, z] (or polar angle),and ϕ is the azimuthal angle (or roll angle). In some instances, d maybe represented by a radial distance, r, such as where the coordinatesystem is a spherical coordinate system.

As such, the illustration 400 of FIG. 4 represents the coordinateconventions that allow modeling of a camera as a function of C (afunction that models or represents the camera) that maps 3D rays (θ, ϕ)(e.g., cast in the direction of objects and/or features) to 2D locationson the image plane (u, v). If an object or feature lies at a 3Ddirection (θ, ϕ), its image on the camera sensor will be located atpixel (u, v)=C(θ, ϕ), where C is a function that represents or modelsthe camera. As a result, a 2-vector (3D direction) is taken as input togenerate a 2-vector (2D position on sensor). Similarly, because C isinvertible, the inverse [θ, ϕ]=C⁻¹(u, v) exists.

Partial derivatives of C may be used to compute a local magnificationfactor, m, as represented by equation (1), below:

$\begin{matrix}{{m\left( {u,v} \right)} = {norm\left\{ {{\frac{d}{du}{C_{\theta}^{- 1}\left( {u,v} \right)}},{\frac{d}{du}{C_{\varnothing}^{- 1}\left( {u,v} \right)}},{\frac{d}{dv}{C_{\theta}^{- 1}\left( {u,v} \right)}},{\frac{d}{du}{C_{\varnothing}^{- 1}\left( {u,v} \right)}}} \right\}}} & (1)\end{matrix}$

where the inverse function, C⁻¹, is split into two functions, asrepresented by equations (2) and (3), below:

θ=C _(θ) ⁻¹(u,v)  (2)

Ø=C _(Ø) ⁻¹(u,v)  (3)

In some embodiments, the initial layers of the machine learning model(s)104 tasked with feature extraction, object detection (in embodimentswhere this feature is internal to the tasks of the machine learningmodel(s) 104), and/or other tasks, may be scale-invariant and work welleven without camera adaptation 112. As a result, the camera informationdetermined using camera adaptation 112 may be injected deeper into thenetwork (e.g., at one or more layers further into the architecture ofthe machine learning model(s), after the feature extraction, objectdetection, and/or other layers), where the feature map sizes may beconsiderably smaller than at earlier layers. The input feature map ofthese deeper layers (e.g., convolutional layers) may be augmented withm(u, v) to enable the layers tasked with depth regression to learn toadjust to the camera model.

Ultimately, a single feature map, m(u, v), may be generated and suppliedto the machine learning model(s) 104 as an extra cue for resolving thedependency of how objects look through different cameras. This mayenable a single machine learning model(s) 104 to predict distancesreliably from images obtained with different cameras having differentcamera parameters, such as different fields of view. During training,multiple cameras may then be used with spatial augmentation (zoom)applied to learn a robust depth regressor. Spatial augmentationtransforms not only the images, but also the camera model function, C,or its inverse. During inference, as described in more detail herein,the camera model may be used to compute the fixed (e.g., in deployment,the camera used may be constant) magnification feature map, m(u, v),which may then be concatenated with the input feature maps generated byone or more layers (e.g., convolutions layers) of the machine learningmodel(s) 104.

The ground truth encoding 110 with camera adaptation 112 is illustrated,as non-limiting examples, in FIGS. 5A and 5B. For example, FIG. 5Aincludes visualizations of ground truth masks and depth map predictionsof a machine learning model(s) based on varying sensor parameters, inaccordance with some embodiments of the present disclosure. Ground truthdepth maps 502A, 502B, and 502C are example visualization of the groundtruth depth maps 222 of FIG. 2 , and correspond respectively to images506A, 506B, and 506C. As an example, the image 506A may have beencaptured using a 120 degree field of view camera, the image 506B mayhave been captured using a 60 degree field of view camera, and the image506C may have been captured using a 30 degree field of view camera.Predicted depth maps 504A, 504B, and 504C correspond to the predictionsof the machine learning model(s) 104, respectively, with respect to theimages 506A, 506B, and 506C. Objects 508A, 508B, and 508C are all atapproximately the same absolute distance from the reference location(e.g., the camera, one or more other sensors, a reference location onthe vehicle 2100, etc.), but appear differently in size or dimension inthe images 506A, 506B, and 506C due to the different fields of view.However, as illustrated by the circles in each of the predicted depthmaps 504, the objects 508 are all correctly predicted to beapproximately the same absolute distance from the reference location.Random zoom augmentation may also be applied to the images 506 and,because the camera model may be adapted based on the augmentation, thepredicted depth maps 504 will still correctly identify the objects 508.

Although not highlighted or identified with circles in FIG. 5A forclarity purposes, each of the other objects in the images 506 are alsorepresented in the ground truth depth maps 502 as well as the predicteddepth maps 504.

As examples of scaling and distortion maps, FIG. 5B includesillustrations of distortion maps and histograms for sensors havingvarying parameters, in accordance with some embodiments of the presentdisclosure. For example, distortion map 520A may represent a distortionmap for a camera having a 30 degree field of view, distortion map 520Bmay represent a distortion map for a camera having a 60 degree field ofview, and distortion map 520C may represent a distortion map for acamera having a 120 degree field of view. Associated therewith arescaling maps 522A, 522B, and 522C, respectively, that correspond to theamount of scaling of the depth or distance values through the field ofview of the camera (e.g., as represented in image-space). For example,with respect to the distortion map 520A, the scaling factors from thescaling map 522A are all less than 1.0, to account for the increasedsize of the objects with respect to a reference camera. As anotherexample, with respect to the distortion map 520C, the scaling factorsfrom the scaling map 522C include values greater than 1.0 to account forthe seemingly smaller size of the objects as captured with a camerahaving a 120 degree field of view, especially at around the outerportions of the field of view. Histograms 542A, 524B, and 524Ccorresponding to the distortion maps 520A, 520B, and 520C, respectively,illustrate the scale changes corresponding to the distortion maps 520.

Referring again to FIG. 1 , the machine learning model(s) 104 may use asinput one or more images (or other data representations) represented bythe sensor data 102 to generate the object distance(s) 106 (e.g.,represented as a depth map in image-space) and/or object detections(e.g., locations of bounding shapes corresponding to objects and/orobstacles depicted in the sensor data 102)) as output. In a non-limitingexample, the machine learning model(s) 104 may take as input an image(s)represented by the pre-processed sensor data and/or the sensor data 102,and may use the sensor data to regress on the distances(s) 106corresponding to objects or obstacles depicted in the image.

In some non-limiting embodiments, the machine learning model(s) 104 mayfurther be trained to intrinsically predict the locations of boundingshapes as the object detection(s) 116 (in other embodiments, an externalobject detection algorithm or network may be used to generate thebounding shapes). In some such examples, the machine learning model(s)104 may be trained to regress on a centroid of a bounding shape(s)corresponding to each object, as well as four bounding shape edgelocations (e.g., four pixel distances to the edges of the boundingshape(s) from the centroid). The machine learning model(s) 104, whenpredicting the bounding shapes, may thus output a first channelcorresponding to a mask channel that includes confidences for pixels,where higher confidences (e.g., of 1) indicate a centroid of a boundingshape. In addition to the mask channel, additional channels (e.g., fouradditional channels) may be output by the machine learning model(s) 104that correspond to the distances to edges of the bounding shape (e.g., adistance upward along a column of pixels from the centroid, a distancedownward along the column of pixels from the centroid, a distance to theright along a row of pixels from the centroid, and a distance to theleft along a row of pixels from the centroid). In other embodiments, themachine learning model(s) 104 may output other representations of thebounding shape locations, such as a location of bounding shape edges inan image and dimensions of the edges, locations of vertices of boundingshapes in an image, and/or other output representations.

In examples where the machine learning model(s) 104 is trained topredict the bounding shapes, the ground truth encoding 110 may furtherinclude encoding the locations of the bounding shapes generated by anobject detector (e.g., the object detector 214) as ground truth data. Insome embodiments, a class of object or obstacle may also be encoded asground truth and associated with each bounding shape. For example, wherean object is a vehicle, a classification of vehicle, vehicle type,vehicle color, vehicle make, vehicle model, vehicle year, and/or otherinformation may be associated with the bounding shape corresponding tothe vehicle.

Although examples are described herein with respect to using neuralnetworks, and specifically convolutional neural networks, as the machinelearning model(s) 104 (e.g., as described in more detail herein withrespect to FIG. 7 ), this is not intended to be limiting. For example,and without limitation, the machine learning model(s) 104 describedherein may include any type of machine learning model, such as a machinelearning model(s) using linear regression, logistic regression, decisiontrees, support vector machines (SVM), Naïve Bayes, k-nearest neighbor(Knn), K means clustering, random forest, dimensionality reductionalgorithms, gradient boosting algorithms, neural networks (e.g.,auto-encoders, convolutional, recurrent, perceptrons, Long/Short TermMemory (LSTM), Hopfield, Boltzmann, deep belief, deconvolutional,generative adversarial, liquid state machine, etc.), and/or other typesof machine learning models.

In some embodiments, the machine learning model(s) 104 may include aconvolutional layer structure, including layers such as those describedherein. For example, the machine learning model(s) 104 may include afull architecture formulated for the task of generating the output(s)114. In other examples, an existing or generated machine learningmodel(s) 104 designed for object detection may be used, and additionallayers—e.g., convolutional layers, such as those described herein—may beinserted into the existing model (e.g., as a head). For example, featureextractor layers may be used to generate feature maps corresponding tothe sensor data 102 that is provided as input to the machine learningmodel(s) 104. The feature maps may then be applied to a first stream oflayers, or a first head, of the machine learning model(s) 104 taskedwith object detection (e.g., computing the object detection(s) 116)and/or may be applied to a second stream of layers, or a second head, ofthe machine learning model(s) 104 tasked with distance estimation (e.g.,computing the object distance(s) 106). As such, where the machinelearning model(s) 104 is designed to generate both the objectdistance(s) 106 and the object detection(s) 116, the machine learningmodel(s) 104 may include at least two streams of layers (or two heads)at some point within the architecture of the machine learning model(s)104.

The machine learning model(s) 104 may use sensor data 102 (and/orpre-processed sensor data) as an input. The sensor data 102 may includeimages representing image data generated by one or more cameras (e.g.,one or more of the cameras described herein with respect to FIGS.21A-21C). For example, the sensor data 102 may include image datarepresentative of a field of view of the camera(s). More specifically,the sensor data 102 may include individual images generated by thecamera(s), where image data representative of one or more of theindividual images may be input into the machine learning model(s) 104 ateach iteration.

The sensor data 102 may be input as a single image, or may be inputusing batching, such as mini-batching. For example, two or more imagesmay be used as inputs together (e.g., at the same time). The two or moreimages may be from two or more sensors (e.g., two or more cameras) thatcaptured the images at the same time.

The sensor data 102 and/or pre-processed sensor data may be input into afeature extractor layer(s) of the machine learning model(s), and then,in embodiments where object detection is intrinsic to the machinelearning model(s) 104, the output of the feature extractor layers may beprovided as input to object detection layers of the machine learningmodel(s) 104. Additional layers—e.g., after the feature extractor layersand/or the object detection layers—of the machine learning model(s) 104may regress on the distances 106 corresponding to object or obstaclesdepicted in the sensor data 102.

One or more of the layers may include an input layer. The input layermay hold values associated with the sensor data 102 and/or pre-processedsensor data. For example, when the sensor data 102 is an image(s), theinput layer may hold values representative of the raw pixel values ofthe image(s) as a volume (e.g., a width, W, a height, H, and colorchannels, C (e.g., RGB), such as 32×32×3), and/or a batch size, B (e.g.,where batching is used)

One or more layers of the machine learning model(s) 104 may includeconvolutional layers. The convolutional layers may compute the output ofneurons that are connected to local regions in an input layer (e.g., theinput layer), each neuron computing a dot product between their weightsand a small region they are connected to in the input volume. A resultof a convolutional layer may be another volume, with one of thedimensions based on the number of filters applied (e.g., the width, theheight, and the number of filters, such as 32×32× 12, if 12 were thenumber of filters).

One or more of the layers may include a rectified linear unit (ReLU)layer. The ReLU layer(s) may apply an elementwise activation function,such as the max (0, x), thresholding at zero, for example. The resultingvolume of a ReLU layer may be the same as the volume of the input of theReLU layer.

One or more of the layers may include a pooling layer. The pooling layermay perform a down-sampling operation along the spatial dimensions(e.g., the height and the width), which may result in a smaller volumethan the input of the pooling layer (e.g., 16×16×12 from the 32×32×12input volume). In some examples, the machine learning model(s) 104 maynot include any pooling layers. In such examples, strided convolutionlayers may be used in place of pooling layers. In some examples, thefeature extractor layer(s) 126 may include alternating convolutionallayers and pooling layers.

One or more of the layers may include a fully connected layer. Eachneuron in the fully connected layer(s) may be connected to each of theneurons in the previous volume. The fully connected layer may computeclass scores, and the resulting volume may be 1×1× number of classes. Insome example, no fully connected layers may be used by the machinelearning model(s) 104 as a whole, in an effort to increase processingtimes and reduce computing resource requirements. In such examples,where no fully connected layers are used, the machine learning model(s)104 may be referred to as a fully convolutional network.

One or more of the layers may, in some examples, include deconvolutionallayer(s). However, the use of the term deconvolutional may be misleadingand is not intended to be limiting. For example, the deconvolutionallayer(s) may alternatively be referred to as transposed convolutionallayers or fractionally strided convolutional layers. The deconvolutionallayer(s) may be used to perform up-sampling on the output of a priorlayer. For example, the deconvolutional layer(s) may be used toup-sample to a spatial resolution that is equal to the spatialresolution of the input images (e.g., the sensor data 102) to themachine learning model(s) 104, or used to up-sample to the input spatialresolution of a next layer.

Although input layers, convolutional layers, pooling layers, ReLUlayers, deconvolutional layers, and fully connected layers are discussedherein with respect to the machine learning model(s) 104, this is notintended to be limiting. For example, additional or alternative layersmay be used, such as normalization layers, SoftMax layers, and/or otherlayer types.

Different orders and numbers of the layers of the machine learningmodel(s) 104 may be used depending on the embodiment. In addition, someof the layers may include parameters (e.g., weights and/or biases),while others may not, such as the ReLU layers and pooling layers, forexample. In some examples, the parameters may be learned by the machinelearning model(s) 104 during training. Further, some of the layers mayinclude additional hyper-parameters (e.g., learning rate, stride,epochs, kernel size, number of filters, type of pooling for poolinglayers, etc.)—such as the convolutional layer(s), the deconvolutionallayer(s), and the pooling layer(s)—while other layers may not, such asthe ReLU layer(s). Various activation functions may be used, includingbut not limited to, ReLU, leaky ReLU, sigmoid, hyperbolic tangent(tanh), exponential linear unit (ELU), etc. The parameters,hyper-parameters, and/or activation functions are not to be limited andmay differ depending on the embodiment.

During training, the sensor data 102—e.g., image data representative ofimages captured by one or more cameras having one or more cameraparameters—may be applied to the machine learning model(s) 104. In somenon-limiting embodiments, as described herein, the scaling and/ordistortion maps may be applied to the machine learning model(s) 104 asanother input, as indicated the dashed line from camera adaptation 112to the machine learning model(s) 104. The machine learning model(s) 104may predict the object distance(s) 106 and/or the object detection(s)116 (e.g., in embodiments where the machine learning model(s) 104 aretrained to predict bounding shapes corresponding to objects orobstacles). The predictions may be compared against the ground truththat is generated during ground truth encoding 110, an example of whichis explained herein at least with reference to FIG. 2 . In someexamples, the ground truth data may be augmented by camera adaptation112, as described herein, while in other embodiments camera adaptation112 may not be executed on the ground truth data (as indicated by thedashed lines). A loss function(s) 108 may be used to compare the groundtruth to the output(s) 114 of the machine learning model(s) 104.

For example, the machine learning model(s) 104 may be trained with thetraining images using multiple iterations until the value of a lossfunction(s) 108 of the machine learning model(s) 104 is below athreshold loss value (e.g., acceptable loss). The loss function(s) 108may be used to measure error in the predictions of the machine learningmodel(s) 104 using ground truth data. In some non-limiting examples, across entropy loss function (e.g., binary cross entropy), an L1 lossfunction, a mean square error loss function, a quadratic loss function,an L2 loss function, a mean absolute error loss function, a mean biasloss function, a hinge loss function, and/or a negative log lossfunction may be used.

Now referring to FIG. 6 , each block of method 600, described herein,comprises a computing process that may be performed using anycombination of hardware, firmware, and/or software. For instance,various functions may be carried out by a processor executinginstructions stored in memory. The method 600 may also be embodied ascomputer-usable instructions stored on computer storage media. Themethod 600 may be provided by a standalone application, a service orhosted service (standalone or in combination with another hostedservice), or a plug-in to another product, to name a few. In addition,method 600 is described, by way of example, with respect to the process100 of FIG. 1 . However, this method 600 may additionally oralternatively be executed by any one system, or any combination ofsystems, including, but not limited to, those described herein.

FIG. 6 is a flow diagram showing a method 600 for training a machinelearning model(s) to predict distances to objects and/or obstacles in anenvironment, in accordance with some embodiments of the presentdisclosure. The method 600, at block B602, includes receiving first datarepresentative of LIDAR information, second data representative of RADARinformation, and third data representative of an image. For example, thesensor data 102 may be received (and/or generated), where the sensordata 102 may include RADAR data, LIDAR data, SONAR data, image datarepresentative of an image(s), and/or other sensor data types.

The method 600, at block B604, includes receiving fourth datarepresentative of a bounding shape corresponding to an object depictedin the image. For example, with reference to FIG. 2 , the objectdetector 214 may generate data corresponding to locations of objects asdenoted by bounding shapes corresponding to the image.

The method 600, at block B 606, includes correlating, with the boundingshape, depth information determined based at least in part on one orboth of the LIDAR information or the RADAR information. For example, thedepth information from the LIDAR data, RADAR data, and/or other sensordata types may be correlated with (e.g., automatically, in embodiments)the bounding shapes corresponding to objects or obstacles depicted inthe image.

The method 600, at block B608, includes generating fifth datarepresentative of ground truth information, the fifth data generatedbased at least in part on converting the depth information to a depthmap. For example, the depth information corresponding to the boundingshapes may be used to generate the ground truth depth map 222 (FIG. 2 ).

The method 600, at block B610, includes training a neural network tocompute the predicted depth map using the fifth data. For example, themachine learning model(s) 104 may be trained, using the ground truthdepth map 222 as ground truth, to generate the object distance(s) 106corresponding to objects and/or obstacles depicted in images.

Machine Learning Model(s) for Predicting Distances to Objects

Now referring to FIG. 7 , FIG. 7 is a data flow diagram for a process700 of predicting distances to objects and/or obstacles in anenvironment using a machine learning model(s), in accordance with someembodiments of the present disclosure. The sensor data 702 may includesimilar sensor data to that described herein at least with respect toFIGS. 1 and 2 . However, in some embodiments, the sensor data 702applied to the machine learning model(s) 104 in deployment may be imagedata only. For example, using the process 100 of FIG. 1 the machinelearning model(s) 104 may be trained to accurately predict the objectdistance(s) 106 and/or the object detection(s) 116 using image dataalone. In such embodiments, the image data may be generated by one ormore cameras (e.g., a single monocular camera, in embodiments, such as awide view camera 2170 of FIG. 21B, multiple camera(s), etc.).

The sensor data 102 may undergo camera adaptation 704, in embodiments.For example, similar to camera adaptation 112 of FIG. 1 , at inference,the camera model may be used to compute a fixed magnification featuremap, m(u, v), that may be used by the machine learning model(s) 104. Forexample, in some embodiments, scaling and/or distortion maps (such asthose illustrated as examples in FIG. 5B) may be applied to the machinelearning model(s) 104 as an additional input. However, because the samecamera(s) may be used in a deployment instance (e.g., for the vehicle2100, the same camera(s) may be used to generate the image data), thescaling and/or distortion maps may be fixed, or the same, throughoutdeployment. As such, in some non-limiting embodiments, the fixedmagnification feature map may be concatenated to convolutional layerinput feature maps.

The sensor data 702 and/or scaling and/or distortion maps generated fromcamera adaptation 704 may be applied to the machine learning model(s)104. The machine learning model(s) 104 (described in more detail hereinwith respect to FIG. 1 ) may use the sensor data 702 and/or the scalingand/or distortion maps to generate the output(s) 114. The output(s) 114may include the object distance(s) 106 and/or the object detection(s)116.

The object distance(s) 106 may be computed as depth or distance valuescorresponding to pixels of the image. For example, for at least pixelsof the image corresponding to objects or obstacles, depth values may becomputed to generate a depth map corresponding to distances to objectsor obstacles depicted in the image. As described herein, the depthvalues may correspond to a z-direction, which may be interpreted as adistance from the reference location (e.g., from the camera) to anobject or obstacle at least partially represented by a given pixel.

The object detection(s) 116, as described herein, may be intrinsic tothe machine learning model(s) 104 and/or may be computed by an objectdetector separate from the machine learning model(s) 104. Where themachine learning model(s) 104 is trained to generate the objectdetection(s) 116, the machine learning model(s) 104 may output datacorresponding to locations of bounding shapes for objects or obstaclesdepicted in images. In a non-limiting embodiments, the machine learningmodel(s) 104 may include multiple output channels corresponding to theobject detection(s) 116. For example, an output may correspond to a maskchannel having values indicating confidences for pixels that correspondto centroids of bounding shapes. Each pixel—or at least pixels havinghigh confidences (e.g., 1, yes, etc.) for a centroid—may also include anumber (e.g., 4) output channels corresponding to locations of, or pixeldistances to, edges of the bounding shape corresponding to the centroid.For example, a first channel may include a pixel distance to a top edgealong a column of pixels including the predicted or regressed centroid,a second channel may include a pixel distance to a right edge along arow of pixels including the predicted or regressed centroid, a thirdchannel may include a pixel distance to a left edge along a row ofpixels including the predicted or regressed centroid, and a fourthchannel may include a pixel distance to a bottom edge along a column ofpixels including the predicted or regressed centroid. As such, thisinformation may be used to generate one or more bounding shapes fordetected objects or obstacles in each image. In other embodiments, themachine learning model(s) 104 may output the bounding shape predictionsas locations of vertices of the bounding shapes, or locations of acentroid and dimensions of the bounding shapes, etc.

In embodiments where the object detections are not predictedintrinsically by the machine learning model(s) 104, the objectdetections may be generated or computed by an object detector 708 (e.g.,similar to the object detector 214 of FIG. 2 ). In such examples, thelocations of the bounding shapes corresponding to objects may becomputed similarly to the description herein for bounding shapes, suchas locations of vertices, locations of a centroid and distances toedges, pixel locations for each pixel within a bounding shape, or eachpixel along edges of the bounding shapes, etc.

A decoder 706 may use the output(s) 114 and/or the outputs of the objectdetector 708 to determine a correlation between the depth values fromthe object distance(s) 106 (e.g., from the predicted depth map) and thebounding shape(s) corresponding to the objects. For example, where asingle bounding shape is computed for an object, the distance valuescorresponding to pixels of the image within the bounding shape of theobject may be used by the decoder 706 to determine the distance to theobject. In some examples, where the distance values vary over the pixelsof the bounding shape, the distance values may be averaged, weighted,and/or a single distance value may be selected for the object. Innon-limiting embodiments, each bounding shape proposal may be associatedwith a single pixel in the depth map (e.g., representing the objectdistance(s) 106). The single pixel may be a central pixel (e.g., thecentroid pixel), or may be another pixel, such as a pixel with thehighest confidence of being associated with the object.

In some examples, using the machine learning model(s) 104 or the objectdetector 708 for the object detections, there may be multiple objectdetections—e.g., bounding shapes—generated for a single object. Eachobject detection for the object may be associated with a differentpixel(s) from the depth map, thereby leading to multiple potentiallocations of the object and potentially varying distance values for theobject. To consolidate the multiple object detection proposals into asingle object detection prediction per physical object instance, theproposals may be clustered—e.g., using density-based spatial clusteringof applications with noise (DBSCAN) algorithm—into a single cluster ofpredictions per physical object instance. The final single objectdetections per physical object instance may be obtained by formingaverages of the individual bounding shapes and distance predictionswithin each cluster. As a result, a single bounding shape may bedetermined for each object that is detected, and a single depth valuemay be associated with the bounding shape for the object.

In some embodiments, each of the pixels within the final bounding shapemay be associated with the depth value, and the locations of the pixelsin world-space may be determined such that the vehicle 2100 is able touse this distance information in world-space to perform one or moreoperations. The one or more operations may include updating a worldmodel, performing path planning, determining one or more controls fornavigating the vehicle 2100 according to the path, updating safetyprocedure information to ensure safety maneuvers are available to thevehicle 2100 without collision, and/or for other operations.

FIGS. 8A-8B are visualizations of object detections and depthpredictions based on outputs of a machine learning model(s), inaccordance with some embodiments of the present disclosure. For example,FIGS. 8A-8B include images 802A and 802B, which may be representationsof image data (e.g., the sensor data 702) that is applied to the machinelearning model(s) 104. Visualizations 804A and 804B, corresponding tothe images 802A and 802B, respectively, represent bounding shapescorresponding to objects (e.g., vehicles) in the images 802A and 802Bthat are detected by the machine learning model(s) 104 and/or the objectdetector 708. Depth maps 806A and 806B, corresponding to the images 802Aand 802B, respectively, represent the object distance(s) 106 predictedby the machine learning model(s) 104. As such, the decoder 706 maycorrelate the locations of bounding shapes within the visualizations804A and 804B to the distance values represented in the depth maps 806Aand 806B, respectively. The result for the vehicle 2100 may be distancesfrom the vehicle 2100 (or a reference location thereof, such as thecamera, or another location of the vehicle 2100) to each of the objectshaving associated bounding shapes.

In some embodiments, due to noise and/or entropy in the training of themachine learning model(s) 104, the machine learning model(s) 104 mayoccasionally output incorrect distance values at inference or deploymenttime. For example, where a small bounding shape of a detected far awayobject overlaps with a larger bounding shape of a detected close-rangeobject, the distance to both objects may be incorrectly predicted to bethe same. As such, in some embodiments, a post-processing algorithm(s)may be applied by a safety bounder 710 to ensure that the machinelearning model(s) 104 computed object distance(s) 106 fall within asafety-permissible range of values. The safety permissible band in whichthe object distance(s) 106 should lie in—e.g., the minimum and maximumdistance estimate values that are accepted as safety-permissible—may beobtained from visual cues in the sensor data 702. For example, usefulcues may be the road shape and/or bounding shapes corresponding toobjects or obstacles (e.g., as predicted by the machine learningmodel(s) 104 and/or the object detector 708). Further description of thesafety bounds computations are described herein at least with respect toFIGS. 10A-13 .

Now referring to FIG. 9 , each block of method 900, described herein,comprises a computing process that may be performed using anycombination of hardware, firmware, and/or software. For instance,various functions may be carried out by a processor executinginstructions stored in memory. The method 900 may also be embodied ascomputer-usable instructions stored on computer storage media. Themethod 900 may be provided by a standalone application, a service orhosted service (standalone or in combination with another hostedservice), or a plug-in to another product, to name a few. In addition,method 900 is described, by way of example, with respect to the process700 of FIG. 7 . However, this method 900 may additionally oralternatively be executed by any one system, or any combination ofsystems, including, but not limited to, those described herein.

FIG. 9 is a flow diagram showing a method 900 for predicting distancesto objects and/or obstacles in an environment using a machine learningmodel(s), in accordance with some embodiments of the present disclosure.The method 900, at block B902, includes applying first datarepresentative of an image of a field of view of an image sensor to aneural network, the neural network trained based at least in part onsecond data representative of ground truth information generated usingat least one of a LIDAR sensor or a RADAR sensor. For example, thesensor data 702 (and/or data representing the scaling and/or distortionmap from camera adaptation 704) may be applied to the machine learningmodel(s) 104. As described herein, the machine learning model(s) 104 maybe trained using ground truth generated—automatically, without humanlabeling or annotation, in embodiments—using LIDAR sensors and/or RADARsensors.

The method 900, at block B904, includes computing, using the neuralnetwork and based at least in part on the first data, third datarepresentative of depth values corresponding to an image. For example,the machine learning model(s) 104 may compute the object distance(s) 106(or a depth map representative thereof).

The method 900, at block B906, includes determining one or more pixelsof the image that corresponding to a bounding shape of an objectdepicted in the image. For example, the decoder 706 may determine acorrelation between a bounding shape(s)—predicted by the machinelearning model(s) 104 and/or the object detector 708—and a depthvalue(s) from the object distance(s) 106 predicted by the machinelearning model(s) 104.

The method 900, at block B908, includes associating, with the object, adepth value of the depth values that corresponds to the one or morepixels. For example, for each object having an associated boundingshape, an object distance(s) 106 may be assigned to the object.

Safety Bounds Computation for Clamping Distance Values

Given the accuracy of object detection methods (e.g., via a machinelearning model(s) 104, via an object detector 708, etc.), tight boundingshapes around objects or obstacles in images may be generated with goodaccuracy. As such, based on camera calibration parameters, it ispossible to compute the path a light ray takes through 3D, world-space,in order to create a pixel in a target location in image-space. A targetlocation may include a location corresponding to a bounding shape, suchas a location within a bounding shape, a location along an edge of abounding shape, or, in some embodiments, a bottom midpoint of a boundingshape (e.g., a point on a bottom, lower edge of a bounding shape).Assuming that the bottom midpoint of the bounding shape is on a groundplane (or a driving surface, with respect to a vehicle or drivingenvironment), the light ray may intersect the ground at this point. Ifroad curvature or grade is known, the radial distance may be computeddirectly. However, accurately predicting road curvature, especiallyusing image data, presents a challenge.

As such, in some embodiments, even when lacking the direct informationof the actual road curvature or grade, a maximum upwards risingcurvature and a maximum downwards rising curvature may be assumed for adriving surface—such as by using regulations on road grade as a guide.The curvatures may be viewed as the extreme boundary walls for theactual road curvature, such that these curvatures may be referred to assafety boundaries. As such, an intersection of the light ray traced froma camera through these maximum curvature points gives the minimum andmaximum limits that the object distance(s) 106 may fall within in orderto be considered safety-permissible. In order to compute a minimum roadcurvature and a maximum road curvature, a light ray trajectory may beintersected with road curvature trajectories (e.g., as estimated byclosed-form equations, which in one embodiment, may be approximated aslinear (illustrated in FIGS. 10B and 10D), and are bounded by automotiveregulations for road grade limits). As a result, the object distance(s)106 from the machine learning model(s) 104 may be clamped to besafety-permissible by ensuring that the object distance(s) 106 fallwithin the value range determined by the minimum and maximum values. Forexample, where an object distance(s) 106 is less than the minimum, theobject distance(s) 106 may be updated or clamped to be the minimum, andwhere an object distance(s) 106 is greater than the maximum, the objectdistance(s) 106 may be updated or clamped to be the maximum. In 3Dworld-space, in some embodiments, the physical boundaries governed byroad curvature may be shaped like bowls.

As an example, and with respect to FIG. 10A, FIG. 10A is a chart 1000illustrating a calculation of safety bounds for clamping distancepredictions of a machine learning model(s), in accordance with someembodiments of the present disclosure. The chart 1000 includes an upwardcurve 1002 representing the determined maximum upward curvature, adownward curve 1004 representing the determined maximum downwardcurvature, and a ground plane 1008. The upward curvature and thedownward curvature help to define the safety-permissible band fordetermining a maximum value 1012 (e.g., maximum distance value) and aminimum value 1012 (e.g., minimum distance value). A ray 1006 may beprojected from a camera through a point—e.g., a bottom centerpoint—corresponding to a bounding shape of an object, which may beestimated to be on the ground plane 1008. As the ray 1006 projects intoworld-space, the ray 1006 intersects the upward curve 1002 to define theminimum value 1012 and intersects the downward curve 1004 to define themaximum value 1010.

Now referring to FIG. 10B, FIG. 10B is a chart 1020 illustrating amaximum upward contour for safety bounds computations, in accordancewith some embodiments of the present disclosure. For example, the chart1020 includes examples of generating or defining the upward curve 1002of FIG. 10A that is used for defining the minimum value 1012. In someembodiments, as described herein, it may be more practical to have asmoothly rising wall, or upward curve 1002, that is limited after athreshold. This may be a result of distances becoming increasinglystretched closer to the horizon. It is, however, important to tune theparameters of the upward curve 1002 carefully because, if the upwardcurve 1002 curves upwards too much, the minimum value 1012 gives morefreedom for uncertainties. The minimum value 1012 should also not be sotight as to reject accurate measurement at near distances. In someexamples, for every smoothly rising upward curve 1002, a linear curve1022 may be constructed by joining ends of the smooth upward curve 1002.In some non-limiting embodiments, this simple linear curve 1022 may beused as the upward curve 1002 for the safety boundary that defines theminimum value 1012. In FIG. 10B, several potential selections for theupward curve 1002 are illustrated. Upward curve 1002A represents theupward curve 1002 with less slope than upward curve 1002B. Vertical wall1024 may represent the extreme upward curve for parameter setting.

Now referring to FIG. 10C, FIG. 10C is an illustration of calculating anupper safety bounds, in accordance with some embodiments of the presentdisclosure. In at least one embodiment, as described herein, the linearcurve 1022 may be determined by connecting ends of the upward curve 1002(e.g., 1002A or 1002B), and may be used as the upward curve 1002 for thepurposes of computing the minimum value 1012. For example, in suchembodiments, there may be two parameters that govern the structure ofthe linear curve 1022— an angle of inclination, ϑ, and a radial distancecap, λ. The angle of inclination, ϑ, may be written as a function ofboundary parameters, D, to simplify the equations. Let, h, be the heightof the camera (e.g., from a center point, or other reference location onthe camera) to the ground plane 1008. The wall may be assumed to riselinearly with an angle of ϑ, where the angle of inclination, ϑ, is givenby equation (4), below:

$\begin{matrix}{{\tan(\theta)} = \frac{h}{d}} & (4)\end{matrix}$

As such, the inclination of the linear curve 1022 may be changed byvarying D. The ray 1006 from the camera may intersect the ground plane1008 at a point, O, which may be at the radial distance, d, from theradial axis. The same ray may intersect the wall at a point, P, and tofind the radial distance to the point, P, equations (5)-(9), below, maybe used. The value for d may be obtained by using a flat groundassumption to compute the radial distance. Here, the triangle formed byPAO=ϑ, and the triangle formed by POA=ϑ1.

$\begin{matrix}{{\tan\left( \theta_{1} \right)} = \frac{h}{D}} & (5)\end{matrix}$

The following equations (6)-(7) may be observed by applying the sinerule to the triangle PAO:

$\begin{matrix}{x = {d\frac{\sin\left( \theta_{1} \right)}{\sin\left( {\theta_{1} + \theta} \right)}}} & (6)\end{matrix}$ $\begin{matrix}{r = {{x{\cos(\theta)}} = \frac{d}{1 + \frac{\tan(\theta)}{\tan\left( \theta_{1} \right)}}}} & (7)\end{matrix}$

Since λ is the maximum radial distance cap,

$\begin{matrix}{{r = \frac{d}{1 + \frac{d}{D}}},{{{if}d} < \frac{\lambda}{1 - \frac{\lambda}{D}}}} & (8)\end{matrix}$ $\begin{matrix}{{r = \lambda},{otherwise}} & (9)\end{matrix}$

Now referring to FIG. 10D, FIG. 10D is a chart 1030 illustrating amaximum downward contour for safety bounds computations, in accordancewith some embodiments of the present disclosure. The downward curve 1004is used to define the maximum value 1012. Similarly to the upward curve1002, for every smooth downwards rising wall, or downward curve 1004, asimple linear curve 1032 may be generated that exhibits some flexibilityon the maximum value 1012. In some examples, for every smoothly risingdownward curve 1004, a linear curve 1032 may be constructed by joiningends of the smooth downward curve 1004. In some non-limitingembodiments, this simple linear curve 1032 may be used as the downwardcurve 1004 for the safety boundary that defines the maximum value 1010.In FIG. 10D, several potential selections for the downward curve 1004are illustrated. Downward curve 1004A represents one example of thedownward curve 1004, however additional downward curves 1004 with moreor less slope may be contemplated without departing from the scope ofthe present disclosure. Vertical wall 1034 may represent the extremedownward curve for parameter setting.

Now with reference to FIG. 10E, FIG. 10E is an illustration ofcalculating a lower safety bounds, in accordance with some embodimentsof the present disclosure. In at least one embodiment, as describedherein, the linear curve 1032 may be determined by connecting ends ofthe downward curve 1004 (e.g., 1004A), and may be used as the downwardcurve 1004 for the purposes of computing the maximum value 1010. Forexample, in such embodiments, there may be two parameters that governthe structure of the linear curve 1032—an angle of inclination, ϑ, and aradial distance cap, λ. The angle of inclination, ϑ, may be written as afunction of boundary parameters, D, to simplify the equations. Let, h,be the height of the camera (e.g., from a center point, or otherreference location on the camera) to the ground plane 1008. The wall maybe assumed to rise linearly with an angle of ϑ, where the angle ofinclination, ϑ, is given by equation (4), described herein. As such, theinclination of the linear curve 1032 may be changed by varying D. Theray 1006 from the camera may intersect the ground plane 1008 at a point,O, which may be at the radial distance, d, from the radial axis. Thesame ray may intersect the wall at a point, P, and to find the radialdistance to the point, P, equation (5), described herein, and equations(10)-(13), below, may be used. The value for d may be obtained by usinga flat ground assumption to compute the radial distance. Here, thetriangle formed by PAO=ϑ, and the triangle formed by COA=ϑ1. Thefollowing equations (10)-(11) may be observed by applying the sine ruleto the triangle PAO:

$\begin{matrix}{x = {d\frac{\sin\left( \theta_{1} \right)}{\sin\left( {\theta_{1} - \theta} \right)}}} & (10)\end{matrix}$ $\begin{matrix}{r = {{x{\cos(\theta)}} = \frac{d}{1 - \frac{\tan(\theta)}{\tan\left( \theta_{1} \right)}}}} & (11)\end{matrix}$

Since λ is the maximum radial distance cap,

$\begin{matrix}{{r = \frac{d}{1 - \frac{d}{D}}},{{{if}d} < \frac{\lambda}{1 - \frac{\lambda}{D}}}} & (12)\end{matrix}$ $\begin{matrix}{{r = \lambda},{otherwise}} & (13)\end{matrix}$

Now referring to FIG. 10F, FIG. 10F is an illustration of a safety bandprofile, in accordance with some embodiments of the present disclosure.For example, it may also be important to look at a safety band 1040,which may essentially be the difference between the maximum value 1010and the minimum value 1012 as the ray moves along flat ground. At neardistances, the safety band 1040 should be tight and should increase asthe obstacle or object moves further away.

Now referring to FIG. 11 , each block of method 1100, described herein,comprises a computing process that may be performed using anycombination of hardware, firmware, and/or software. For instance,various functions may be carried out by a processor executinginstructions stored in memory. The method 1100 may also be embodied ascomputer-usable instructions stored on computer storage media. Themethod 1100 may be provided by a standalone application, a service orhosted service (standalone or in combination with another hostedservice), or a plug-in to another product, to name a few. In addition,method 1100 is described, by way of example, with respect to FIGS.10A-10F. The method 1100 may be executed by any one system, or anycombination of systems, including, but not limited to, those describedherein.

FIG. 11 is a flow diagram showing a method 1100 for safety boundsdeterminations using road shape, in accordance with some embodiments ofthe present disclosure. The method 1100, at block B1102, includesreceiving an image (or other sensor data 702) at the machine learningmodel(s) 104. The machine learning model(s) 104 (and/or the objectdetector 708) may output a bounding shape which may be used at blockB1104 to estimate the minimum value 1012 from the upward curve 1102 andused at block B1106 to estimate the maximum value 1010 from the downwardcurve 1104. The maximum value 1010 and the minimum value 1012 may definethe safety bounds (e.g., a range of distance values between the minimumvalue 1012 and the maximum value 1010 may be safety-permissible distancevalues) for the particular object at the particular object instancecorresponding to the bounding shape. At block B1102, the machinelearning model(s) 104 may output an object distance(s) 106, as apredicted distance corresponding to the object instance represented bythe bounding shape. At block B1108, a comparison may be done between theobject distance(s) 106 and the safety bounds defined by the minimumvalue 1012 and the maximum value 1012. If the object distance(s) 106falls within the safety bounds (e.g., is greater than the minimum value1012 and less than the maximum value 1010), the object distance(s) 106may be determined to be a safe distance at block B1108, and may bepassed to block B1112 to indicate to the system that the objectdistance(s) 106 is acceptable. If the object distance(s) 106 fallsoutside of the safety bounds (e.g., is less than the minimum value 1012or more than the maximum value 1010), the object distance(s) 106 may bedetermined not to be a safe distance at block B1108. When the objectdistance(s) 106 is outside of the safety bounds, the object distance(s)106 may be clamped to the safety bounds at block B1110. For example,where the object distance(s) 106 is less than the minimum value 1012,the object distance(s) 106 may be updated to be the minimum value 1012.Similarly, where the object distance(s) 106 is greater than the maximumvalue 1010, the object distance(s) 106 may be updated to the maximumvalue 1010. Once updated, the updated distance(s) may be passed to blockB1112 to indicate to the system that the updated distance(s) isacceptable.

Now referring to FIG. 12 , FIG. 12 is an illustration of calculatingsafety bounds using a bounding shape corresponding to an object, inaccordance with some embodiments of the present disclosure. For example,as described herein, another method of determining safety bounds—thatmay be used separately from, or in conjunction with, safety bounds fromroad shape—is using a bounding shape of an object or obstacle. FIG. 12may represent a pinhole camera model. Using a focal length, f, a realheight, H, of an object 1204, a bounding shape height, h, and aprojection function, F, then a distance, d, to an object may becalculated using equation (14), below:

d=F(f,H,h)  (14)

In some embodiments, distance, d, to an object may be inverselyproportional to the bounding shape height, h, which is typically thepinhole camera model. As such, the distance, d, may be calculated usingequation (15), below, which may be referred to as the distance model:

$\begin{matrix}{d = {f*\frac{H}{h}}} & (15)\end{matrix}$

Object detection and distance to object estimation may provide distances({d₀, d₁, d₂, . . . , d_(t)}) and bounding shape heights ({({h₀, h₁, h₂,. . . , h_(t)}). Therefore, a real height, H, of an object may becalculated using, for example, linear regression between d and 1/h.After sufficiently many samples are collected, the value of d inequation (15) may be estimated from h. A safety lower bound, or minimumvalue, d_(L), and a safety upper bound, or maximum value, d_(U), may bedetermined using, for example, equations (16) and (17), below:

$\begin{matrix}{d_{L} = {{f*\frac{H}{h}} - e}} & (16)\end{matrix}$ $\begin{matrix}{d_{U} = {{f*\frac{H}{h}} + e}} & (17)\end{matrix}$

where e is a predefined safety margin. As such, the real height, H, ofthe object may be estimated using modeling (e.g., equation (15)) basedon d and h data samples over time.

The bounding shape height, h, and the focal length, f, may be replacedby an angle, α, between the optical rays at the middle-top andmiddle-bottom of the bounding shape, as represented by equation (18),below:

$\begin{matrix}{d = \frac{H}{2*{\tan(\alpha)}}} & (18)\end{matrix}$

The inverse camera model may then be applied directly to the boundingshape to obtain the angle, α. Thus, the real object height, H, may beestimated using equation (18) based on samples of d and α over time forany known camera model.

Now referring to FIG. 13 , each block of method 1300, described herein,comprises a computing process that may be performed using anycombination of hardware, firmware, and/or software. For instance,various functions may be carried out by a processor executinginstructions stored in memory. The method 1300 may also be embodied ascomputer-usable instructions stored on computer storage media. Themethod 1300 may be provided by a standalone application, a service orhosted service (standalone or in combination with another hostedservice), or a plug-in to another product, to name a few. In addition,method 1300 is described, by way of example, with respect to FIG. 12 .The method 1300 may be executed by any one system, or any combination ofsystems, including, but not limited to, those described herein.

FIG. 13 a flow diagram showing a method 1300 for safety boundsdeterminations using bounding shape properties, in accordance with someembodiments of the present disclosure. The method 1300, at block B1302,includes receiving an image (or other sensor data 702) at the machinelearning model(s) 104. The machine learning model(s) 104 (and/or theobject detector 708) may output a bounding shape which may be used atblock B1304 for computing a distance, d, according to the distance modelof equation (15), described herein. The distance, d may then be used atblock B1306 to compute safety bounds (e.g., a minimum value, d_(L), anda maximum value, d_(U)), such as, for example, according to equations(16) and (17), described herein. At block B1302, the machine learningmodel(s) 104 may output an object distance(s) 106, as a predicteddistance corresponding to the object instance represented by thebounding shape. At block B1308, a comparison may be done between theobject distance(s) 106 and the safety bounds defined by the minimumvalue, d_(L), and the maximum value, d_(U). If the object distance(s)106 falls within the safety bounds (e.g., is greater than the minimumvalue and less than the maximum value), the object distance(s) 106 maybe determined to be a safe distance at block B1308, and may be passed toblock B1312 to indicate to the system that the object distance(s) 106 isacceptable. In addition, where the object distance(s) 106 is acceptable,the information may be passed back to the distance model to furthertrain or update the distance model, as described herein with respect toFIG. 12 . If the object distance(s) 106 falls outside of the safetybounds (e.g., is less than the minimum value or more than the maximumvalue), the object distance(s) 106 may be determined not to be a safedistance at block B1308. When the object distance(s) 106 is outside ofthe safety bounds, the object distance(s) 106 may be clamped to thesafety bounds at block B3110. For example, where the object distance(s)106 is less than the minimum value, the object distance(s) 106 may beupdated to be the minimum value. Similarly, where the object distance(s)106 is greater than the maximum value, the object distance(s) 106 may beupdated to the maximum value. Once updated, the updated distance(s) maybe passed to block B1312 to indicate to the system that the updateddistance(s) is acceptable.

Training a Machine Learning Model(s) for Predicting Distances to aFree-Space Boundary

In addition to, or alternatively from, the process 100 described herein,a process 1400 may be executed in order to train a machine learningmodel(s) 104 for predicting depth or distance information to portions ofan environment other than objects (e.g., vehicles, pedestrians,bicyclists, etc.). For example, in addition to, alternatively from,training the machine learning model(s) 104 to predict the objectdistance(s) 106 and/or the object detection(s) 116, the machine learningmodel(s) 104 may be trained to predict free-space distance(s) 1408 toone or more free-space boundaries and/or other distance(s) 1410—such asdistances to portions of the environment that are not the free-spaceboundary or objects (e.g., the driving surface, buildings, trees, etc.).As such, the machine learning model(s) 104 may be trained to predict theobject distance(s) 106, the free-space distance(s) 1408, and/or otherdistance(s) 1410. In addition, similar to the description of the machinelearning model(s) 104 herein, the machine learning model(s) 104 may betrained such that, in deployment, image data alone may be provided as aninput to the machine learning model(s) 104. As such, by leveragingsensor data 102 (e.g., LIDAR data, RADAR data, image data, etc.),free-space data 1402, and/or ego-motion data 1404 during training, themachine learning model(s) 104 may accurately predict the objectdistance(s) 106, the object detection(s) 116, the free-space distance(s)1408, and/or the distance(s) 1410 using image data alone as an input.However, this is not intended to be limiting, and in some non-limitingembodiments, the machine learning model(s) 104 may generate predictionsof the object distance(s) 106, the object detection(s) 116, thefree-space distance(s) 1408, and/or the distance(s) 1410 using any typeof sensor data 102, such as but not limited to those described herein.

With reference to FIG. 14 , the sensor data 102 (similar to the sensordata 102 described herein at least with respect to at FIGS. 1 and 2 )may be generated by one or more sensors of the vehicle 2100. The sensordata 102 may be used to generate free-space data 1402 and/or ego-motiondata 1404, and may also be used for ground truth encoding 110. Thefree-space data 1402 may include location information (e.g., pixellocations) of a free-space boundary(ies) within the environment asdepicted by images. For example, the free-space data 1402 may include afree-space boundary that divides drivable free-space for the vehicle2100 from non-drivable space. As an example illustration, free-spaceboundary 1504 may represent the free-space boundary within theenvironment as depicted in visualization 1502 of FIG. 15A. Thefree-space boundary 1504 may provide an indication to the vehicle 2100that the vehicle 2100 may not safely traverse portions of theenvironment beyond the free-space boundary 1504 (e.g., the vehicle 2100may not drive into other vehicles, may not drive off of the road, etc.).In some non-limiting embodiments, the free-space data 1402 may begenerated using a computer vision algorithm, a machine learningmodel(s), a neural network(s), an object detection algorithm, and/oranother type of free-space boundary detection algorithm. For anon-limiting example, the free-space data 1402 may be generated similarto the description in U.S. Non-Provisional application Ser. No.16/355,328, filed on Mar. 15, 2019, which is hereby incorporated byreference in its entirety.

In some embodiments, and with reference to FIG. 15A, a free-spaceboundary 1506 may be determined using a flat ground assumption—e.g.,assuming that the driving surface is flat and has no contour. In suchexamples, the free-space boundary 1506, and thus the depth or distanceinformation to the free-space boundary 1506 that is used for groundtruth generation, may be determined in view of the flat groundassumption. However, using a flat-ground assumption may result in lessaccurate and reliable free-space distance(s)—or depth maps—for groundtruth encoding 110. As a result, the predictions of the machine learningmodel(s) 104 in deployment may be less accurate than when the contour,curve, or other profile information of the driving surface is accountedfor in generating the ground truth depth maps during ground truthencoding 110. As such, in some embodiments, profile information of thedriving surface may be accounted for using the ego-motion data 1404, thefree-space data 1402, and/or the sensor data 102—e.g., LIDAR data. Usingthe profile information may result in distances or depths to afree-space boundary 1504 that are more accurate for training or tuningthe machine learning model(s) 104 to predict the free-space distance(s)1408.

In some embodiments, the free-space data 1402 may include pixellocations within the images represented by the sensor data 102 (e.g.,image data generated by one or more cameras of the vehicle 2100, such asa front-facing monocular camera(s)). As described herein, the pixellocations may be predictions of one or more free-space algorithms, suchas but not limited to those described herein. The pixel locationscorrespond to two-dimensional (2D) pixel locations within image-space,and the corresponding 3D location in world-space may be determined usingintrinsic and/or extrinsic parameters of the camera and/or other sensorsof the vehicle 2100. For example, a ray may be cast from the camera tothe location of the pixel in world-space that corresponds to thefree-space boundary. The location of the vehicle 2100 at the time theimage is captured may be assumed to be (0, 0, 0), or may be actualthree-dimensional (3D) location values for some origin point of thevehicle 2100 (e.g., the center of an axle, a front most point of thevehicle 2100, etc.). As such, as the vehicle 2100 traverses theenvironment, the location of the origin point of the vehicle 2100 overtime (e.g., as represented by ego-motion trajectory 1508 in FIG. 15A,which may represent an accumulation of 3D motion of the vehicle 2100)may be tracked such that once the vehicle 2100 reaches the point inworld-space that corresponds to the free-space boundary, the locationinformation may be used to determine the profile information for thedriving surface at that point—or for a plane including that point. Thus,the elevation, curve, change in position, contour, and/or other profileinformation corresponding to the driving surface may be determined usingthe ego-motion data 1404. As a result, the ego-motion data 1404 may beused to generate a representation of the profile of the ground plane ordriving surface such that—since the free-space boundary points areassumed to be located on the driving surface—the predictions of thedistances or depths to the free-space boundary are more accurate.

Thus, in contrast to a flat-ground approach, the future position of thevehicle 2100 may inform the system of the actual or more accurateprofile of the driving surface as depicted in the image(s), and theactual profile of the driving surface may be used to update the groundtruth depth or distance values corresponding to the free-space boundary1504. With respect to the visualization 1510, the free-space boundary1504 that corresponds to the actual road profile is different from thefree-space boundary 1506 generated using the flat ground assumption.This may be a result of the upward curvature of the driving surface, asdepicted in the visualization 1502. The free-space boundary 1504 (and/orthe free-space boundary 1506) may be projected into image-space, and maybe compared to sensor data 102 (e.g., a LIDAR point cloud 1512)—inimage-space, in embodiments—to determine the depth or distance to thefree-space boundary 1504 (and/or the free-space boundary 1506) forgenerating a ground truth depth map. For example, the LIDAR point cloud1512 may be projected into the image-space, such that the LIDAR pointsthat correspond to the pixels along the free-space boundary 1504 may bedetermined to be the depth or distance to the portion of the free-spaceboundary 1504 that correspond to the pixel. In some embodiments, todetermine the ground truth depth or distance values from the sensor data102—e.g., from the LIDAR point cloud—sampling 1406 may be used, asdescribed in more detail herein.

As an example, let (X₀, Y₀, Z₀), (X₁, Y₁, Z₁), . . . , (X_(n), Y_(n),Z_(n)) be the future trajectory of the vehicle 2100 in a rig coordinatesystem with respect to the vehicle 2100 at a particular timestamp (wheren is the number of 3D points). Here (X₀, Y₀, Z₀)=(0, 0, 0) based on acurrent setting. All the points on this ordered trajectory of thevehicle 2100 are projected to Y=0 plane in the rig coordinate system.Now, assuming a piecewise linear model, the ordered set of projections(X_(p0), 0, Z_(p0)), (X_(p1), 0, Z_(p1)), . . . , (X_(pn), 0, Z_(pn)),may represent the road shape. To determine the distance to thefree-space boundary given the road shape, let (X_(cp0), Y_(cp0),Z_(cp0)), (X_(cp1), Y_(cp1), Z_(cp1)), . . . , (X_(cpn), Y_(cpn),Z_(cpn)) be the ordered set of projected points in the camera coordinatesystem. These projected points may be referred to as pivot points.Normalized projections into the image-space may be computed for thepivot points as (X_(cp0)/Z_(cp0), Y_(cp0)/Z_(cp0)). Y projection binsB₀, B₁, . . . , B_(k) corresponding to K pivot points may be generatedby enforcing the condition of increasing normalized Y on the ordered setof projections. The pivot points which don't fall into this conditionmay be filtered, in some embodiments. As such, the normal of the planeat each filtered pivot point may be determined according to equations(19) and (20), below:

$\begin{matrix}{\left( {n_{x},n_{y},n_{z}} \right) = \left( {0,{- \frac{dz}{D}},\frac{dy}{D}} \right)} & (19)\end{matrix}$ $\begin{matrix}{{{dy} = {Y_{cpi} - Y_{{cp}({i - 1})}}},{{dz} = {Z_{cpi} - Z_{{cp}({i - 1})}}},{D = \sqrt{{dy}^{2} + {dz}^{2}}}} & (20)\end{matrix}$

where (i−1) is the previous point in the original ordered set. Eachfree-space boundary point (x_(f), y_(f)) may be classified into one ofthe bins, B, based on its normalized Y projection. The actual 3Dfree-space boundary point may be computed by the intersection of the raycorresponding to the 2D point and the plane corresponding to each bin.As such, the ego-motion data 1404 may be used to determine a piecewiseplanar driving surface or ground plane that is different—and moreaccurate than—an estimate flat ground plane.

Ultimately, the depth values or distance values may be encoded duringground truth encoding 110 to generate a depth map corresponding to thefree-space boundary 1504. As another example, and with respect to image520 of FIG. 15B, a truck 1522 may be captured using a camera and/oranother sensor(s) of the vehicle 2100. The pixels corresponding to afree-space boundary may be determined from the free-space data 1402 andthe ego-motion data 1404 (e.g., accumulated 3D motion information) ofthe vehicle 2100 may be used to determine more accurate location datafor the free-space boundary when taking into account the profileinformation of the driving surface. The updated free-space boundary(e.g., updated to account for the profile information) may be comparedto other sensor data 102 (e.g., a LIDAR point cloud) to determinedistances or depths to the updated free-space boundary. Once thisinformation is known, a ground truth depth map 1524 may be generated toinclude ground truth depth or distance information corresponding to thefree-space boundary for the image 1520. For example, the ground truthdepth map 1524 may include encoded depth values 1526 that correspond tothe updated free-space boundary. This ground truth depth map may be usedto train the machine learning model(s) 104 to generate more accuratepredictions of the free-space distance(s) 1408.

In some embodiments, the machine learning model(s) 104 may be trained topredict the distance(s) 1410, which may correspond to portions of theenvironment where an object is not detected (e.g., an object the machinelearning model(s) 104 is trained to detect, such as vehicles,pedestrians, etc.) and/or that do not correspond to the free-spaceboundary. In some embodiments, the sensor data 102 (e.g., LIDAR data,SONAR data, RADAR data, etc.) may be used to determine the distance(s)1410 for generating a ground truth depth map corresponding to theseportions of the environment. For example, with reference tovisualization 1602 of FIG. 16A, a LIDAR point cloud 1604 may beprojected into image-space, along with a free-space boundary 1606 and/orone or more objects 1608 (e.g., vehicles 1608A and 1608B). In someembodiments, the LIDAR point cloud 1604 (or other sensor data type) maybe projected over only a portion of the image, such as a bottom half, abottom ⅓, a bottom ⅔, and/or another cropped portion of the image. Thismay be to reduce the amount of processing for portions of theenvironment that may be outside of concern of a vehicle's trajectorythrough the environment (e.g., the sky, upper levels of buildings,etc.,) and/or because the accuracy or availability of the sensor datamay only extend to a certain potion of the environment (e.g., LIDAR datamay be most accurate within 40 meters of the vehicle 2100, and thus allLIDAR points in the point cloud beyond 40 meters may be cropped out). Insome embodiments, the filtering of the LIDAR point cloud 1604 may beexecuted such that only portions of the environment where desired depthor distance information is desired remain. For example, points of theLIDAR point cloud 1604 that correspond to the sky, mountains, or otherbackground scenery may be filtered out (e.g., by manual ormachine-assisted filtering), and points corresponding to trees,sidewalks, signs, and/or other portion of the environment may not befiltered out.

In some non-limiting embodiments, in addition or alternatively fromcropping out a portion of the sensor data, the portions of the sensordata 102 that correspond to the objects—or bounding shapes thereof—maybe cropped, filtered, or otherwise ignored, and/or the portions of thesensor data 102 along the free-space boundary 1606 may be cropped,filtered, or otherwise ignored. As an example, visualization 1620includes the sensor data 102 (e.g., the LIDAR point cloud 1604)projected into image-space along with the free-space boundary 1606 andbounding shapes corresponding to the vehicles 1608 (e.g., vehicles 1608Aand 1608B, among others). As illustrated in FIG. 16B, the points of theLIDAR point cloud 1604 within the bounding shapes and along andimmediately adjacent the free-space boundary 1606 have been removed. Thedepth or distance values corresponding to the remaining points in theLIDAR point cloud 1604 (and/or other sensor data types, whereapplicable) may be used to generate a ground truth depth map, similar tothose described herein, that corresponds to the portions of theenvironment not associated with detected objects and/or a free-spaceboundary(ies). This ground truth depth map may be used to train themachine learning model(s) 104 to generate more accurate predictions ofthe distance(s) 1410.

As an example, and with respect to FIGS. 16C-16D, a ground truth depthmap 1642 may be generated using the sensor data 102 that corresponds toimage 1630. For example, the sensor data 102 (e.g., LIDAR data, RADARdata, SONAR data, etc.) may be used to determine distances to a tree1632, a sign 1634, a driving surface 1636, and/or other portions of theenvironment where a depth and/or distance estimation is desired. In suchan example, the sensor data corresponding to the sky 1638, a free-spaceboundary (not shown), one or more vehicles 1640, and/or other portionsof the environment may be filtered out such that this information is notused in computing the ground truth depth map 1642. The illustration ofthe ground truth depth map 1642 may correspond to a depth map generatedusing LIDAR data, such that the ground truth depth map substantiallymirrors the projection of the LIDAR point cloud into image-space. Assuch, the ground truth depth map 1642 may be used to train the machinelearning model(s) 104 to predict the distance(s) 1410.

The machine learning model(s) 104 may be trained with the trainingimages using multiple iterations until the value of a loss function(s)108 of the machine learning model(s) 104 is below a threshold loss value(e.g., acceptable loss). The loss function(s) 108 may be used to measureerror in the predictions of the machine learning model(s) 104 usingground truth data. In some non-limiting examples, different lossfunctions 108 may be used for different predictions. For example, afirst loss function 108 may be used for object distance(s) 106, a secondloss function 108 may be used for free-space distance(s) 1408, and/or athird loss function may be used for distance(s) 1410. As a non-limitingexample, the first loss function may include a coverage-based L1 lossfunction scaled by an inverse of the area, the second loss function mayinclude a point-wise loss function averaged over the free-space boundarypoints, and the third loss function may include a point-wise L1 lossaveraged over the dense LIDAR points. As described herein, a differentground truth depth map may be generated for each differentprediction—e.g., a first ground truth depth map for objects (such asdescribed with respect to FIG. 5A), a second ground truth depth map forthe free-space boundary (such as described with respect to FIG. 15C),and/or a third ground truth depth map for other portions of theenvironment (e.g., the background, the driving surface, buildings,trees, etc., such as described with respect to FIG. 16D). In someembodiments, a single ground truth depth map may represent each of thepredictions—e.g., a ground truth depth map for objects (such asdescribed with respect to FIG. 5A), the free-space boundary (such asdescribed with respect to FIG. 15C), and/or other portions of theenvironment (e.g., the background, the driving surface, buildings,trees, etc., such as described with respect to FIG. 16D). In still otherembodiments, one or more predictions may be represented by a firstground truth depth map, one or more predictions may be represented by asecond ground truth depth map, and so on.

As a result, in either embodiment (e.g., combined depth map or separatedepth maps) the loss function(s) 108 may be a combination of differentcomponent loss functions 108, where the components come from pixelsbelonging to objects, a free-space boundary(ies), and/or background orother portions of the environment. For the loss function 108corresponding to the object distance(s) 106, for each pixel, i, if p_(i)is the predicted depth value, d_(i) is the ground truth depth value, andw_(i), is the weightage, the loss may be computed using one or more ofequations (21)-(23), below:

$\begin{matrix}\begin{matrix}{L_{o} = {\sum}_{i = 0}^{N}} & {w_{i}{❘{p_{i} - d_{i}}❘}}\end{matrix} & (21)\end{matrix}$ $\begin{matrix}{{w_{i} = 0},{{{if}i} \in \phi}} & (22)\end{matrix}$ $\begin{matrix}{{w_{i} = \frac{1}{A}},{{{if}i} \in O},{A{is}{the}{area}{of}{object}O}} & (23)\end{matrix}$

For the free-space distance(s) 1408 and/or the distance(s) 1410,pointwise loss functions 108 may be used, as described in more detailbelow. The final loss, L_(F), may be a combination of the object loss,L_(O), pointwise loss corresponding to the free-space distance(s) 1408,L_(FS), and/or pointwise loss corresponding to the distance(s) 1410,L_(B). As such, the total or final loss, L_(F), may be computedaccording to equation (24), below:

L=L _(O) +αL _(FS) +βL _(B)  (24)

where α is a weight of the free-space loss, L_(FS), corresponding to thefree-space distance(s) 1408 and β is a weight of the background loss,L_(B), corresponding to the distance(s) 1410.

In order to compute L_(FS) and L_(B), sampling 1406 may be used in someembodiments. For example, such as where the output of the machinelearning model(s) 104 is down-sampled (e.g., at a lower spatialresolution) with respect to the input of the machine learning model(s)104, sampling 1406 may be used to convert outputs of the machinelearning model(s) 104 to the input resolution for training purposes. Forexample, because rasterizing the free-space points and the sparse LIDARpoints (or other sensor data points) to generate a ground truth depthmap may be a challenging task, sampling 1406 may be used to remove theneed for rasterizing. As such, sampling 1406 may be used for convertinga depth map(s)—e.g., for the free-space distance(s) 1408 and/or thedistance(s) 1410—as predicted by the machine learning model(s) 104 to aspatial resolution that corresponds to the ground truth depth map(s)generated during ground truth encoding 110. As a result, the spatialresolution of the predicted depth map(s) from the machine learningmodel(s) 104 may correspond the spatial resolution of the input images(or other sensor data 102) such that the loss function(s) 108 may usethe predicted depth map(s) and the ground truth depth map(s) at the samespatial resolution during training of the machine learning model(s) 104.

As an example, and with respect to FIG. 17 , a free-space distance 1408A(and/or a distance 1410) may be predicted by the machine learningmodel(s) 104 at a spatial resolution that is four times less than thespatial resolution of the input image and thus the corresponding groundtruth depth map. The spatial resolution being four times less is forexample purposes only, and the output resolution could be two times, sixtimes, eight times, sixteen times, etc. less than or more than the inputspatial resolution, or there may be no down-sampling or up-sampling,without departing from the scope of the present disclosure. Thefree-space distance 1408A may be at a location (u, v) in the predicteddepth map from the machine learning model(s) 104. However, because theoutput may be down-sampled—e.g., four times in this example—the location(u, v) in the predicted depth map may not coincide with a point 1702 (orpixel) in the ground truth depth map. As such, sampling 1406 may be usedto determine depth values corresponding to the points 1702 (e.g., points1702A, 1702B, 1702C, and 1702D) at the spatial resolution of the groundtruth depth map. For example, the free-space distance 1408A at thelocation (u, v) may be projected—using sampling 1406— to itscorresponding location at the spatial resolution of the ground truthdepth map, and the four points 1702A-1702D may be determined as the fourclosest neighbor points (or pixels) at the spatial resolution of theground truth depth map. A distance 1704 between each of the points 1702and the location (u, v) may be determined, and the distance 1704 may beused to determine a value for each of the points 1702. For example,where each of the distances 1704 between the points 1702 and thelocation (u, v) were equal, then the free-space distance 1408A may beattributed to each of the points 1702A-1702D. Where the distances arenot equal, bilinear interpolation may be used to determine the weightsfor each of the points 1702, where bilinear interpolation uses thedistances in the calculation. The weights may be used when computingloss using the loss function(s) 108, as described in more detail herein.

For example, for a point, (X_(j), Y_(j)), in an input image, with d_(j)as the ground truth depth value, the corresponding location in theground truth depth map may be represented according to equation (25),below:

$\begin{matrix}\left( {\frac{X_{j}}{s},\frac{Y_{j}}{s}} \right) & (25)\end{matrix}$

where s is the scale factor (4, in the example above). As such, if(x_(j0), y_(j0)), (x_(j0), y_(j1)), (x_(j1), y_(j0)) and (x_(j1),y_(j1)) are the neighbors of the location in the ground truth depth map,and Pjoo′ p_(j01), p_(j10), and p_(j11) are the predictions of thoseneighbors, the loss may be computed according to equation (26), below:

L _(FS) ,L _(B)=Σ_(j=0) ^(N) |p _(j00) w _(j00) +p _(j01) w _(j01) +p_(j10) w _(j10) +p _(j11) w _(j11) −d _(j)|  (26)

where w_(j00), w_(j01), w_(j10), and w_(j11) are bilinear interpolationbased weights.

As a result, for the pointwise loss functions—e.g., for comparingpredicted depth map(s) corresponding to the free-space distance(s) 1408and/or the distance(s) 1410 to the ground truth depth map(s)—thepredicted depth map(s) may be used, in addition to sampling 1406,without requiring rasterizing each of the free-space boundary pointsand/or the LIDAR (or other sensor data 102) points.

Now referring to FIGS. 18-20 , each block of methods 1800, 1900, and2000, described herein, comprises a computing process that may beperformed using any combination of hardware, firmware, and/or software.For instance, various functions may be carried out by a processorexecuting instructions stored in memory. The methods 1800, 1900, and2000 may also be embodied as computer-usable instructions stored oncomputer storage media. The methods 1800, 1900, and 2000 may be providedby a standalone application, a service or hosted service (standalone orin combination with another hosted service), or a plug-in to anotherproduct, to name a few. In addition, methods 1800, 1900, and 2000 aredescribed, by way of example, with respect to the process 1400 of FIG.14 . However, these methods 1800, 1900, and 2000 may additionally oralternatively be executed by any one system, or any combination ofsystems, including, but not limited to, those described herein.

Now referring to FIG. 18 , FIG. 18 is a flow diagram showing a method1800 for predicting—in deployment—distance to obstacles, objects, and/ora detected free-space boundary in an environment, in accordance withsome embodiments of the present disclosure. The method 1800, at blockB1802, includes applying image data to a deployed neural network. Forexample, the sensor data 102—e.g., image data representative of animage—may be applied to the machine learning model(s) 104 after themachine learning model(s) 104 is deployed for use in operation. Asdescribed herein, the machine learning model(s) 104 may be trained usinga plurality of loss functions 108, such as a first loss function for theobject distance(s) 106, a second loss function for the free-spacedistance(s) 1408, and/or a third loss function for the distance(s) 1410.

The method 1800, at block B1804, includes computing, using the deployedneural network, a depth map comprising first depth data corresponding toone or more objects depicted in a field of view and second depth datacorresponding to a free-space boundary in the field of view. Forexample, a depth map corresponding to the object distances 106 and thefree-space distances 1408 may be computed by the machine learningmodel(s) 104 based on processing of the image data.

The method 1800, at block B1806, includes associating the first depthdata with the one or more objects and associating the second depth datawith the free-space boundary. For example, the depth values from thedepth maps may be associated with the objects in the environment and/orthe corresponding locations of the free-space boundary within theenvironment.

The method 1800, at block B1808, includes performing one or moreoperations by an ego-vehicle based at least in part on the first depthdata and the second depth data. For example, the vehicle 2100 mayperform one or more operations using the first depth values associatedwith the objects and the second depth values associated with thefree-space boundary. The operations may include path planning, worldmodel management, control decisions, obstacle or collision avoidance,actuation controls, perception, and/or other operations of the vehicle2100 (or another vehicle type, such as an aircraft, a water vessel,etc.).

Now referring to FIG. 19 , FIG. 19 is a flow diagram showing a method1900 for training a machine learning model(s) to predict distances toobstacles, objects, and/or a detected free-space boundary in anenvironment, in accordance with some embodiments of the presentdisclosure. The method 1900, at block B1902, includes receiving imagedata generated by a camera of a vehicle at a time. For example, acamera(s) of the vehicle 2100 may capture image data representative ofan image of an environment in the field(s) of view of the camera(s).

The method 1900, at block B1904, includes receiving first sensor datarepresentative of future motion of the vehicle from the time to a futuretime through at least a portion of an environment as depicted by theimage. For example, the sensor data 102—e.g., from one or more of a GNSSsensor(s) 2158, an IMU sensor(s) 2166, an image sensor of a camera, aspeed sensor(s) 2144, a vibration sensor(s) 2142, a steering sensor(s)2140, and/or another sensor type—may be used to determine the ego-motiondata 1404 representing motion of the vehicle from the origin point whenthe image was captured through at least a portion of the environmentdepicted in the image, as the vehicle 2100 traverses the environment.

The method 1900, at block B1906, includes modeling a ground plane basedat least in part on the first sensor data. For example, the sensor data102 representing the accumulated motion of the vehicle 2100 may be usedto model a ground plane—e.g., as piecewise planar—in order to moreaccurately estimate a location of a free-space boundary as compared tousing a flat ground approach, as described herein.

The method 1900, at block B1908, includes determining an updatedlocation of an updated free-space boundary based at least in part on theground plane and first data representative of an initial location of aninitial free-space boundary. For example, an updated location of thefree-space boundary may be computed using the free-space data 1402 andthe modeled ground plane, as described herein.

The method 1900, at block B1910, includes determining one or more depthvalues corresponding to the updated free-space boundary based at leastin part on second sensor data representative of a LIDAR point cloud andthe updated location of the updated free-space boundary. For example,the sensor data 102—e.g., the LIDAR point cloud, or other sensor data,such as RADAR, SONAR, etc. —may be projected into image-space inaddition to the updated free-space boundary. The depth or distancevalues from the sensor data 102 that are associated with the pixelscorresponding to the updated free-space boundary may be attributed tothe pixels to define a distance to the updated free-space boundarywithin the environment.

The method 1900, at block B1912, includes generating a depth mapcorresponding to the updated free-space boundary. For example, the depthvalues corresponding to the updated free-space boundary may be used togenerate a ground truth depth map, such as the ground truth depth map1524 of FIG. 15C.

The method 1900, at block B1914, includes training a machine learningmodel using the depth map as ground truth data. For example, the groundtruth depth map may be used to train the machine learning model(s) 104using the one or more loss functions 108.

Now referring to FIG. 20 , FIG. 20 is a flow diagram showing a method2000 of sampling depth values from a predicted depth map for training amachine learning model(s), in accordance with some embodiments of thepresent disclosure. The method 2000 may be used, as a non-limitingexample, for training the machine learning model(s) 104 to predict thedistance(s) 1410 and/or the free-space distance(s) 1408.

The method 2000, at block B2002, includes generating a ground truthdepth map corresponding to depth values associated with an image of afirst spatial resolution. For example, a ground truth depth map may begenerated that corresponds to depth values associated with animage—e.g., as represented by the sensor data 102—at a first spatialresolution. As a result, the ground truth depth map may have a samespatial resolution as the image.

The method 2000, at block B2004, includes applying the image datarepresentative of an image to a neural network. For example, the sensordata 102 representative of the image may be applied to the machinelearning model(s) 104.

The method 2000, at block B2006, includes computing, using the neuralnetwork, a predicted depth map at a second spatial resolution differentfrom the first spatial resolution. For example, during processing by themachine learning model(s) 104, the spatial resolution may bedown-sampled or up-sampled, and the output depth map may thus correspondto a down-sampled image or an up-sampled image.

The method 2000, at block B2008, includes determining, for at least onepoint in the predicted depth map having an associated first depth value,corresponding neighbor points in the ground truth depth map. Forexample, the point from the predicted depth map may be projected intothe first spatial resolution such that one or more neighbor points(e.g., four neighbor points, as illustrated in FIG. 17 ) may bedetermined at the first spatial resolution.

The method 2000, at block B2010, includes executing a sampling algorithmto determine associated second depth values corresponding to each of theneighbor points. For example, a sampling algorithm—such as bilinearinterpolation—may be used to determine associated depth values for theneighbor points, or to determine weighted values associated therewith.This determination may be based on a distance of each neighbor pointfrom the point of the predicted depth map.

The method 2000, at block B2012, includes training the neural networkbased at least in part on a comparison between the associated seconddepth values and the ground truth depth values corresponding to theneighbor points in the ground truth depth map. For example, the machinelearning model(s) 104 may be trained—e.g., with the loss function(s)108— using the associated depth values determined from the predicteddepth map and correlated to the first spatial resolution of the groundtruth depth map.

Example Autonomous Vehicle

FIG. 21A is an illustration of an example autonomous vehicle 2100, inaccordance with some embodiments of the present disclosure. Theautonomous vehicle 2100 (alternatively referred to herein as the“vehicle 2100”) may include, without limitation, a passenger vehicle,such as a car, a truck, a bus, a first responder vehicle, a shuttle, anelectric or motorized bicycle, a motorcycle, a fire truck, a policevehicle, an ambulance, a boat, a construction vehicle, an underwatercraft, a drone, and/or another type of vehicle (e.g., that is unmannedand/or that accommodates one or more passengers). Autonomous vehiclesare generally described in terms of automation levels, defined by theNational Highway Traffic Safety Administration (NHTSA), a division ofthe US Department of Transportation, and the Society of AutomotiveEngineers (SAE) “Taxonomy and Definitions for Terms Related to DrivingAutomation Systems for On-Road Motor Vehicles” (Standard No.J3016-201806, published on Jun. 15, 2018, Standard No. J3016-201609,published on Sep. 30, 2016, and previous and future versions of thisstandard). The vehicle 2100 may be capable of functionality inaccordance with one or more of Level 3-Level 5 of the autonomous drivinglevels. For example, the vehicle 2100 may be capable of conditionalautomation (Level 3), high automation (Level 4), and/or full automation(Level 5), depending on the embodiment.

The vehicle 2100 may include components such as a chassis, a vehiclebody, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and othercomponents of a vehicle. The vehicle 2100 may include a propulsionsystem 2150, such as an internal combustion engine, hybrid electricpower plant, an all-electric engine, and/or another propulsion systemtype. The propulsion system 2150 may be connected to a drive train ofthe vehicle 2100, which may include a transmission, to enable thepropulsion of the vehicle 2100. The propulsion system 2150 may becontrolled in response to receiving signals from thethrottle/accelerator 2152.

A steering system 2154, which may include a steering wheel, may be usedto steer the vehicle 2100 (e.g., along a desired path or route) when thepropulsion system 2150 is operating (e.g., when the vehicle is inmotion). The steering system 2154 may receive signals from a steeringactuator 2156. The steering wheel may be optional for full automation(Level 5) functionality.

The brake sensor system 2146 may be used to operate the vehicle brakesin response to receiving signals from the brake actuators 2148 and/orbrake sensors.

Controller(s) 2136, which may include one or more system on chips (SoCs)2104 (FIG. 21C) and/or GPU(s), may provide signals (e.g., representativeof commands) to one or more components and/or systems of the vehicle2100. For example, the controller(s) may send signals to operate thevehicle brakes via one or more brake actuators 2148, to operate thesteering system 2154 via one or more steering actuators 2156, to operatethe propulsion system 2150 via one or more throttle/accelerators 2152.The controller(s) 2136 may include one or more onboard (e.g.,integrated) computing devices (e.g., supercomputers) that process sensorsignals, and output operation commands (e.g., signals representingcommands) to enable autonomous driving and/or to assist a human driverin driving the vehicle 2100. The controller(s) 2136 may include a firstcontroller 2136 for autonomous driving functions, a second controller2136 for functional safety functions, a third controller 2136 forartificial intelligence functionality (e.g., computer vision), a fourthcontroller 2136 for infotainment functionality, a fifth controller 2136for redundancy in emergency conditions, and/or other controllers. Insome examples, a single controller 2136 may handle two or more of theabove functionalities, two or more controllers 2136 may handle a singlefunctionality, and/or any combination thereof.

The controller(s) 2136 may provide the signals for controlling one ormore components and/or systems of the vehicle 2100 in response to sensordata received from one or more sensors (e.g., sensor inputs). The sensordata may be received from, for example and without limitation, globalnavigation satellite systems sensor(s) 2158 (e.g., Global PositioningSystem sensor(s)), RADAR sensor(s) 2160, ultrasonic sensor(s) 2162,LIDAR sensor(s) 2164, inertial measurement unit (IMU) sensor(s) 2166(e.g., accelerometer(s), gyroscope(s), magnetic compass(es),magnetometer(s), etc.), microphone(s) 2196, stereo camera(s) 2168,wide-view camera(s) 2170 (e.g., fisheye cameras), infrared camera(s)2172, surround camera(s) 2174 (e.g., 360 degree cameras), long-rangeand/or mid-range camera(s) 2198, speed sensor(s) 2144 (e.g., formeasuring the speed of the vehicle 2100), vibration sensor(s) 2142,steering sensor(s) 2140, brake sensor(s) (e.g., as part of the brakesensor system 2146), and/or other sensor types.

One or more of the controller(s) 2136 may receive inputs (e.g.,represented by input data) from an instrument cluster 2132 of thevehicle 2100 and provide outputs (e.g., represented by output data,display data, etc.) via a human-machine interface (HMI) display 2134, anaudible annunciator, a loudspeaker, and/or via other components of thevehicle 2100. The outputs may include information such as vehiclevelocity, speed, time, map data (e.g., the HD map 2122 of FIG. 21C),location data (e.g., the vehicle's 2100 location, such as on a map),direction, location of other vehicles (e.g., an occupancy grid),information about objects and status of objects as perceived by thecontroller(s) 2136, etc. For example, the HMI display 2134 may displayinformation about the presence of one or more objects (e.g., a streetsign, caution sign, traffic light changing, etc.), and/or informationabout driving maneuvers the vehicle has made, is making, or will make(e.g., changing lanes now, taking exit 34B in two miles, etc.).

The vehicle 2100 further includes a network interface 2124 which may useone or more wireless antenna(s) 2126 and/or modem(s) to communicate overone or more networks. For example, the network interface 2124 may becapable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. Thewireless antenna(s) 2126 may also enable communication between objectsin the environment (e.g., vehicles, mobile devices, etc.), using localarea network(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc.,and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox,etc.

FIG. 21B is an example of camera locations and fields of view for theexample autonomous vehicle 2100 of FIG. 21A, in accordance with someembodiments of the present disclosure. The cameras and respective fieldsof view are one example embodiment and are not intended to be limiting.For example, additional and/or alternative cameras may be includedand/or the cameras may be located at different locations on the vehicle2100.

The camera types for the cameras may include, but are not limited to,digital cameras that may be adapted for use with the components and/orsystems of the vehicle 2100. The camera(s) may operate at automotivesafety integrity level (ASIL) B and/or at another ASIL. The camera typesmay be capable of any image capture rate, such as 60 frames per second(fps), 2120 fps, 240 fps, etc., depending on the embodiment. The camerasmay be capable of using rolling shutters, global shutters, another typeof shutter, or a combination thereof. In some examples, the color filterarray may include a red clear clear clear (RCCC) color filter array, ared clear clear blue (RCCB) color filter array, a red blue green clear(RBGC) color filter array, a Foveon X3 color filter array, a Bayersensors (RGGB) color filter array, a monochrome sensor color filterarray, and/or another type of color filter array. In some embodiments,clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or anRBGC color filter array, may be used in an effort to increase lightsensitivity.

In some examples, one or more of the camera(s) may be used to performadvanced driver assistance systems (ADAS) functions (e.g., as part of aredundant or fail-safe design). For example, a Multi-Function MonoCamera may be installed to provide functions including lane departurewarning, traffic sign assist and intelligent headlamp control. One ormore of the camera(s) (e.g., all of the cameras) may record and provideimage data (e.g., video) simultaneously.

One or more of the cameras may be mounted in a mounting assembly, suchas a custom designed (3-D printed) assembly, in order to cut out straylight and reflections from within the car (e.g., reflections from thedashboard reflected in the windshield mirrors) which may interfere withthe camera's image data capture abilities. With reference to wing-mirrormounting assemblies, the wing-mirror assemblies may be custom 3-Dprinted so that the camera mounting plate matches the shape of thewing-mirror. In some examples, the camera(s) may be integrated into thewing-mirror. For side-view cameras, the camera(s) may also be integratedwithin the four pillars at each corner of the cabin.

Cameras with a field of view that include portions of the environment infront of the vehicle 2100 (e.g., front-facing cameras) may be used forsurround view, to help identify forward facing paths and obstacles, aswell aid in, with the help of one or more controllers 2136 and/orcontrol SoCs, providing information critical to generating an occupancygrid and/or determining the preferred vehicle paths. Front-facingcameras may be used to perform many of the same ADAS functions as LIDAR,including emergency braking, pedestrian detection, and collisionavoidance. Front-facing cameras may also be used for ADAS functions andsystems including Lane Departure Warnings (“LDW”), Autonomous CruiseControl (“ACC”), and/or other functions such as traffic signrecognition.

A variety of cameras may be used in a front-facing configuration,including, for example, a monocular camera platform that includes a CMOS(complementary metal oxide semiconductor) color imager. Another examplemay be a wide-view camera(s) 2170 that may be used to perceive objectscoming into view from the periphery (e.g., pedestrians, crossing trafficor bicycles). Although only one wide-view camera is illustrated in FIG.21B, there may any number of wide-view cameras 2170 on the vehicle 2100.In addition, long-range camera(s) 2198 (e.g., a long-view stereo camerapair) may be used for depth-based object detection, especially forobjects for which a neural network has not yet been trained. Thelong-range camera(s) 2198 may also be used for object detection andclassification, as well as basic object tracking.

One or more stereo cameras 2168 may also be included in a front-facingconfiguration. The stereo camera(s) 2168 may include an integratedcontrol unit comprising a scalable processing unit, which may provide aprogrammable logic (FPGA) and a multi-core micro-processor with anintegrated CAN or Ethernet interface on a single chip. Such a unit maybe used to generate a 3-D map of the vehicle's environment, including adistance estimate for all the points in the image. An alternative stereocamera(s) 2168 may include a compact stereo vision sensor(s) that mayinclude two camera lenses (one each on the left and right) and an imageprocessing chip that may measure the distance from the vehicle to thetarget object and use the generated information (e.g., metadata) toactivate the autonomous emergency braking and lane departure warningfunctions. Other types of stereo camera(s) 2168 may be used in additionto, or alternatively from, those described herein.

Cameras with a field of view that include portions of the environment tothe side of the vehicle 2100 (e.g., side-view cameras) may be used forsurround view, providing information used to create and update theoccupancy grid, as well as to generate side impact collision warnings.For example, surround camera(s) 2174 (e.g., four surround cameras 2174as illustrated in FIG. 21B) may be positioned to on the vehicle 2100.The surround camera(s) 2174 may include wide-view camera(s) 2170,fisheye camera(s), 360 degree camera(s), and/or the like. Four example,four fisheye cameras may be positioned on the vehicle's front, rear, andsides. In an alternative arrangement, the vehicle may use three surroundcamera(s) 2174 (e.g., left, right, and rear), and may leverage one ormore other camera(s) (e.g., a forward-facing camera) as a fourthsurround view camera.

Cameras with a field of view that include portions of the environment tothe rear of the vehicle 2100 (e.g., rear-view cameras) may be used forpark assistance, surround view, rear collision warnings, and creatingand updating the occupancy grid. A wide variety of cameras may be usedincluding, but not limited to, cameras that are also suitable as afront-facing camera(s) (e.g., long-range and/or mid-range camera(s)2198, stereo camera(s) 2168), infrared camera(s) 2172, etc.), asdescribed herein.

FIG. 21C is a block diagram of an example system architecture for theexample autonomous vehicle 2100 of FIG. 21A, in accordance with someembodiments of the present disclosure. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory.

Each of the components, features, and systems of the vehicle 2100 inFIG. 21C are illustrated as being connected via bus 2102. The bus 2102may include a Controller Area Network (CAN) data interface(alternatively referred to herein as a “CAN bus”). A CAN may be anetwork inside the vehicle 2100 used to aid in control of variousfeatures and functionality of the vehicle 2100, such as actuation ofbrakes, acceleration, braking, steering, windshield wipers, etc. A CANbus may be configured to have dozens or even hundreds of nodes, eachwith its own unique identifier (e.g., a CAN ID). The CAN bus may be readto find steering wheel angle, ground speed, engine revolutions perminute (RPMs), button positions, and/or other vehicle status indicators.The CAN bus may be ASIL B compliant.

Although the bus 2102 is described herein as being a CAN bus, this isnot intended to be limiting. For example, in addition to, oralternatively from, the CAN bus, FlexRay and/or Ethernet may be used.Additionally, although a single line is used to represent the bus 2102,this is not intended to be limiting. For example, there may be anynumber of busses 2102, which may include one or more CAN busses, one ormore FlexRay busses, one or more Ethernet busses, and/or one or moreother types of busses using a different protocol. In some examples, twoor more busses 2102 may be used to perform different functions, and/ormay be used for redundancy. For example, a first bus 2102 may be usedfor collision avoidance functionality and a second bus 2102 may be usedfor actuation control. In any example, each bus 2102 may communicatewith any of the components of the vehicle 2100, and two or more busses2102 may communicate with the same components. In some examples, eachSoC 2104, each controller 2136, and/or each computer within the vehiclemay have access to the same input data (e.g., inputs from sensors of thevehicle 2100), and may be connected to a common bus, such the CAN bus.

The vehicle 2100 may include one or more controller(s) 2136, such asthose described herein with respect to FIG. 21A. The controller(s) 2136may be used for a variety of functions. The controller(s) 2136 may becoupled to any of the various other components and systems of thevehicle 2100, and may be used for control of the vehicle 2100,artificial intelligence of the vehicle 2100, infotainment for thevehicle 2100, and/or the like.

The vehicle 2100 may include a system(s) on a chip (SoC) 2104. The SoC2104 may include CPU(s) 2106, GPU(s) 2108, processor(s) 2110, cache(s)2112, accelerator(s) 2114, data store(s) 2116, and/or other componentsand features not illustrated. The SoC(s) 2104 may be used to control thevehicle 2100 in a variety of platforms and systems. For example, theSoC(s) 2104 may be combined in a system (e.g., the system of the vehicle2100) with an HD map 2122 which may obtain map refreshes and/or updatesvia a network interface 2124 from one or more servers (e.g., server(s)2178 of FIG. 21D).

The CPU(s) 2106 may include a CPU cluster or CPU complex (alternativelyreferred to herein as a “CCPLEX”). The CPU(s) 2106 may include multiplecores and/or L2 caches. For example, in some embodiments, the CPU(s)2106 may include eight cores in a coherent multi-processorconfiguration. In some embodiments, the CPU(s) 2106 may include fourdual-core clusters where each cluster has a dedicated L2 cache (e.g., a2 MB L2 cache). The CPU(s) 2106 (e.g., the CCPLEX) may be configured tosupport simultaneous cluster operation enabling any combination of theclusters of the CPU(s) 2106 to be active at any given time.

The CPU(s) 2106 may implement power management capabilities that includeone or more of the following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when the core is not actively executing instructionsdue to execution of WFI/WFE instructions; each core may be independentlypower-gated; each core cluster may be independently clock-gated when allcores are clock-gated or power-gated; and/or each core cluster may beindependently power-gated when all cores are power-gated. The CPU(s)2106 may further implement an enhanced algorithm for managing powerstates, where allowed power states and expected wakeup times arespecified, and the hardware/microcode determines the best power state toenter for the core, cluster, and CCPLEX. The processing cores maysupport simplified power state entry sequences in software with the workoffloaded to microcode.

The GPU(s) 2108 may include an integrated GPU (alternatively referred toherein as an “iGPU”). The GPU(s) 2108 may be programmable and may beefficient for parallel workloads. The GPU(s) 2108, in some examples, mayuse an enhanced tensor instruction set. The GPU(s) 2108 may include oneor more streaming microprocessors, where each streaming microprocessormay include an L1 cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of the streaming microprocessors may share anL2 cache (e.g., an L2 cache with a 512 KB storage capacity). In someembodiments, the GPU(s) 2108 may include at least eight streamingmicroprocessors. The GPU(s) 2108 may use compute application programminginterface(s) (API(s)). In addition, the GPU(s) 2108 may use one or moreparallel computing platforms and/or programming models (e.g., NVIDIA'sCUDA).

The GPU(s) 2108 may be power-optimized for best performance inautomotive and embedded use cases. For example, the GPU(s) 2108 may befabricated on a Fin field-effect transistor (FinFET). However, this isnot intended to be limiting and the GPU(s) 2108 may be fabricated usingother semiconductor manufacturing processes. Each streamingmicroprocessor may incorporate a number of mixed-precision processingcores partitioned into multiple blocks. For example, and withoutlimitation, 64 PF32 cores and 32 PF64 cores may be partitioned into fourprocessing blocks. In such an example, each processing block may beallocated 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, twomixed-precision NVIDIA TENSOR COREs for deep learning matrix arithmetic,an L0 instruction cache, a warp scheduler, a dispatch unit, and/or a 64KB register file. In addition, the streaming microprocessors may includeindependent parallel integer and floating-point data paths to providefor efficient execution of workloads with a mix of computation andaddressing calculations. The streaming microprocessors may includeindependent thread scheduling capability to enable finer-grainsynchronization and cooperation between parallel threads. The streamingmicroprocessors may include a combined L1 data cache and shared memoryunit in order to improve performance while simplifying programming.

The GPU(s) 2108 may include a high bandwidth memory (HBM) and/or a 16 GBHBM2 memory subsystem to provide, in some examples, about 900 GB/secondpeak memory bandwidth. In some examples, in addition to, oralternatively from, the HBM memory, a synchronous graphics random-accessmemory (SGRAM) may be used, such as a graphics double data rate typefive synchronous random-access memory (GDDR5).

The GPU(s) 2108 may include unified memory technology including accesscounters to allow for more accurate migration of memory pages to theprocessor that accesses them most frequently, thereby improvingefficiency for memory ranges shared between processors. In someexamples, address translation services (ATS) support may be used toallow the GPU(s) 2108 to access the CPU(s) 2106 page tables directly. Insuch examples, when the GPU(s) 2108 memory management unit (MMU)experiences a miss, an address translation request may be transmitted tothe CPU(s) 2106. In response, the CPU(s) 2106 may look in its pagetables for the virtual-to-physical mapping for the address and transmitsthe translation back to the GPU(s) 2108. As such, unified memorytechnology may allow a single unified virtual address space for memoryof both the CPU(s) 2106 and the GPU(s) 2108, thereby simplifying theGPU(s) 2108 programming and porting of applications to the GPU(s) 2108.

In addition, the GPU(s) 2108 may include an access counter that may keeptrack of the frequency of access of the GPU(s) 2108 to memory of otherprocessors. The access counter may help ensure that memory pages aremoved to the physical memory of the processor that is accessing thepages most frequently.

The SoC(s) 2104 may include any number of cache(s) 2112, including thosedescribed herein. For example, the cache(s) 2112 may include an L3 cachethat is available to both the CPU(s) 2106 and the GPU(s) 2108 (e.g.,that is connected both the CPU(s) 2106 and the GPU(s) 2108). Thecache(s) 2112 may include a write-back cache that may keep track ofstates of lines, such as by using a cache coherence protocol (e.g., MEI,MESI, MSI, etc.). The L3 cache may include 4 MB or more, depending onthe embodiment, although smaller cache sizes may be used.

The SoC(s) 2104 may include one or more accelerators 2114 (e.g.,hardware accelerators, software accelerators, or a combination thereof).For example, the SoC(s) 2104 may include a hardware acceleration clusterthat may include optimized hardware accelerators and/or large on-chipmemory. The large on-chip memory (e.g., 4 MB of SRAM), may enable thehardware acceleration cluster to accelerate neural networks and othercalculations. The hardware acceleration cluster may be used tocomplement the GPU(s) 2108 and to off-load some of the tasks of theGPU(s) 2108 (e.g., to free up more cycles of the GPU(s) 2108 forperforming other tasks). As an example, the accelerator(s) 2114 may beused for targeted workloads (e.g., perception, convolutional neuralnetworks (CNNs), etc.) that are stable enough to be amenable toacceleration. The term “CNN,” as used herein, may include all types ofCNNs, including region-based or regional convolutional neural networks(RCNNs) and Fast RCNNs (e.g., as used for object detection).

The accelerator(s) 2114 (e.g., the hardware acceleration cluster) mayinclude a deep learning accelerator(s) (DLA). The DLA(s) may include oneor more Tensor processing units (TPUs) that may be configured to providean additional ten trillion operations per second for deep learningapplications and inferencing. The TPUs may be accelerators configuredto, and optimized for, performing image processing functions (e.g., forCNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specificset of neural network types and floating point operations, as well asinferencing. The design of the DLA(s) may provide more performance permillimeter than a general-purpose GPU, and vastly exceeds theperformance of a CPU. The TPU(s) may perform several functions,including a single-instance convolution function, supporting, forexample, INT8, INT16, and FP16 data types for both features and weights,as well as post-processor functions.

The DLA(s) may quickly and efficiently execute neural networks,especially CNNs, on processed or unprocessed data for any of a varietyof functions, including, for example and without limitation: a CNN forobject identification and detection using data from camera sensors; aCNN for distance estimation using data from camera sensors; a CNN foremergency vehicle detection and identification and detection using datafrom microphones; a CNN for facial recognition and vehicle owneridentification using data from camera sensors; and/or a CNN for securityand/or safety related events.

The DLA(s) may perform any function of the GPU(s) 2108, and by using aninference accelerator, for example, a designer may target either theDLA(s) or the GPU(s) 2108 for any function. For example, the designermay focus processing of CNNs and floating point operations on the DLA(s)and leave other functions to the GPU(s) 2108 and/or other accelerator(s)2114.

The accelerator(s) 2114 (e.g., the hardware acceleration cluster) mayinclude a programmable vision accelerator(s) (PVA), which mayalternatively be referred to herein as a computer vision accelerator.The PVA(s) may be designed and configured to accelerate computer visionalgorithms for the advanced driver assistance systems (ADAS), autonomousdriving, and/or augmented reality (AR) and/or virtual reality (VR)applications. The PVA(s) may provide a balance between performance andflexibility. For example, each PVA(s) may include, for example andwithout limitation, any number of reduced instruction set computer(RISC) cores, direct memory access (DMA), and/or any number of vectorprocessors.

The RISC cores may interact with image sensors (e.g., the image sensorsof any of the cameras described herein), image signal processor(s),and/or the like. Each of the RISC cores may include any amount ofmemory. The RISC cores may use any of a number of protocols, dependingon the embodiment. In some examples, the RISC cores may execute areal-time operating system (RTOS). The RISC cores may be implementedusing one or more integrated circuit devices, application specificintegrated circuits (ASICs), and/or memory devices. For example, theRISC cores may include an instruction cache and/or a tightly coupledRAM.

The DMA may enable components of the PVA(s) to access the system memoryindependently of the CPU(s) 2106. The DMA may support any number offeatures used to provide optimization to the PVA including, but notlimited to, supporting multi-dimensional addressing and/or circularaddressing. In some examples, the DMA may support up to six or moredimensions of addressing, which may include block width, block height,block depth, horizontal block stepping, vertical block stepping, and/ordepth stepping.

The vector processors may be programmable processors that may bedesigned to efficiently and flexibly execute programming for computervision algorithms and provide signal processing capabilities. In someexamples, the PVA may include a PVA core and two vector processingsubsystem partitions. The PVA core may include a processor subsystem,DMA engine(s) (e.g., two DMA engines), and/or other peripherals. Thevector processing subsystem may operate as the primary processing engineof the PVA, and may include a vector processing unit (VPU), aninstruction cache, and/or vector memory (e.g., VMEM). A VPU core mayinclude a digital signal processor such as, for example, a singleinstruction, multiple data (SIMD), very long instruction word (VLIW)digital signal processor. The combination of the SIMD and VLIW mayenhance throughput and speed.

Each of the vector processors may include an instruction cache and maybe coupled to dedicated memory. As a result, in some examples, each ofthe vector processors may be configured to execute independently of theother vector processors. In other examples, the vector processors thatare included in a particular PVA may be configured to employ dataparallelism. For example, in some embodiments, the plurality of vectorprocessors included in a single PVA may execute the same computer visionalgorithm, but on different regions of an image. In other examples, thevector processors included in a particular PVA may simultaneouslyexecute different computer vision algorithms, on the same image, or evenexecute different algorithms on sequential images or portions of animage. Among other things, any number of PVAs may be included in thehardware acceleration cluster and any number of vector processors may beincluded in each of the PVAs. In addition, the PVA(s) may includeadditional error correcting code (ECC) memory, to enhance overall systemsafety.

The accelerator(s) 2114 (e.g., the hardware acceleration cluster) mayinclude a computer vision network on-chip and SRAM, for providing ahigh-bandwidth, low latency SRAM for the accelerator(s) 2114. In someexamples, the on-chip memory may include at least 4 MB SRAM, consistingof, for example and without limitation, eight field-configurable memoryblocks, that may be accessible by both the PVA and the DLA. Each pair ofmemory blocks may include an advanced peripheral bus (APB) interface,configuration circuitry, a controller, and a multiplexer. Any type ofmemory may be used. The PVA and DLA may access the memory via a backbonethat provides the PVA and DLA with high-speed access to memory. Thebackbone may include a computer vision network on-chip thatinterconnects the PVA and the DLA to the memory (e.g., using the APB).

The computer vision network on-chip may include an interface thatdetermines, before transmission of any control signal/address/data, thatboth the PVA and the DLA provide ready and valid signals. Such aninterface may provide for separate phases and separate channels fortransmitting control signals/addresses/data, as well as burst-typecommunications for continuous data transfer. This type of interface maycomply with ISO 26262 or IEC 61508 standards, although other standardsand protocols may be used.

In some examples, the SoC(s) 2104 may include a real-time ray-tracinghardware accelerator, such as described in U.S. patent application Ser.No. 16/101,232, filed on Aug. 10, 2018. The real-time ray-tracinghardware accelerator may be used to quickly and efficiently determinethe positions and extents of objects (e.g., within a world model), togenerate real0time visualization simulations, for RADAR signalinterpretation, for sound propagation synthesis and/or analysis, forsimulation of SONAR systems, for general wave propagation simulation,for comparison to LIDAR data for purposes of localization and/or otherfunctions, and/or for other uses.

The accelerator(s) 2114 (e.g., the hardware accelerator cluster) have awide array of uses for autonomous driving. The PVA may be a programmablevision accelerator that may be used for key processing stages in ADASand autonomous vehicles. The PVA's capabilities are a good match foralgorithmic domains needing predictable processing, at low power and lowlatency. In other words, the PVA performs well on semi-dense or denseregular computation, even on small data sets, which need predictablerun-times with low latency and low power. Thus, in the context ofplatforms for autonomous vehicles, the PVAs are designed to run classiccomputer vision algorithms, as they are efficient at object detectionand operating on integer math.

For example, according to one embodiment of the technology, the PVA isused to perform computer stereo vision. A semi-global matching-basedalgorithm may be used in some examples, although this is not intended tobe limiting. Many applications for Level 3-5 autonomous driving requiremotion estimation/stereo matching on-the-fly (e.g., structure frommotion, pedestrian recognition, lane detection, etc.). The PVA mayperform computer stereo vision function on inputs from two monocularcameras.

In some examples, the PVA may be used to perform dense optical flow.According to process raw RADAR data (e.g., using a 4D Fast FourierTransform) to provide Processed RADAR. In other examples, the PVA isused for time of flight depth processing, by processing raw time offlight data to provide processed time of flight data, for example.

The DLA may be used to run any type of network to enhance control anddriving safety, including for example, a neural network that outputs ameasure of confidence for each object detection. Such a confidence valuemay be interpreted as a probability, or as providing a relative “weight”of each detection compared to other detections. This confidence valueenables the system to make further decisions regarding which detectionsshould be considered as true positive detections rather than falsepositive detections. For example, the system may set a threshold valuefor the confidence and consider only the detections exceeding thethreshold value as true positive detections. In an automatic emergencybraking (AEB) system, false positive detections would cause the vehicleto automatically perform emergency braking, which is obviouslyundesirable. Therefore, only the most confident detections should beconsidered as triggers for AEB. The DLA may run a neural network forregressing the confidence value. The neural network may take as itsinput at least some subset of parameters, such as bounding boxdimensions, ground plane estimate obtained (e.g. from anothersubsystem), inertial measurement unit (IMU) sensor 2166 output thatcorrelates with the vehicle 2100 orientation, distance, 3D locationestimates of the object obtained from the neural network and/or othersensors (e.g., LIDAR sensor(s) 2164 or RADAR sensor(s) 2160), amongothers.

The SoC(s) 2104 may include data store(s) 2116 (e.g., memory). The datastore(s) 2116 may be on-chip memory of the SoC(s) 2104, which may storeneural networks to be executed on the GPU and/or the DLA. In someexamples, the data store(s) 2116 may be large enough in capacity tostore multiple instances of neural networks for redundancy and safety.The data store(s) 2112 may comprise L2 or L3 cache(s) 2112. Reference tothe data store(s) 2116 may include reference to the memory associatedwith the PVA, DLA, and/or other accelerator(s) 2114, as describedherein.

The SoC(s) 2104 may include one or more processor(s) 2110 (e.g.,embedded processors). The processor(s) 2110 may include a boot and powermanagement processor that may be a dedicated processor and subsystem tohandle boot power and management functions and related securityenforcement. The boot and power management processor may be a part ofthe SoC(s) 2104 boot sequence and may provide runtime power managementservices. The boot power and management processor may provide clock andvoltage programming, assistance in system low power state transitions,management of SoC(s) 2104 thermals and temperature sensors, and/ormanagement of the SoC(s) 2104 power states. Each temperature sensor maybe implemented as a ring-oscillator whose output frequency isproportional to temperature, and the SoC(s) 2104 may use thering-oscillators to detect temperatures of the CPU(s) 2106, GPU(s) 2108,and/or accelerator(s) 2114. If temperatures are determined to exceed athreshold, the boot and power management processor may enter atemperature fault routine and put the SoC(s) 2104 into a lower powerstate and/or put the vehicle 2100 into a chauffeur to safe stop mode(e.g., bring the vehicle 2100 to a safe stop).

The processor(s) 2110 may further include a set of embedded processorsthat may serve as an audio processing engine. The audio processingengine may be an audio subsystem that enables full hardware support formulti-channel audio over multiple interfaces, and a broad and flexiblerange of audio I/O interfaces. In some examples, the audio processingengine is a dedicated processor core with a digital signal processorwith dedicated RAM.

The processor(s) 2110 may further include an always on processor enginethat may provide necessary hardware features to support low power sensormanagement and wake use cases. The always on processor engine mayinclude a processor core, a tightly coupled RAM, supporting peripherals(e.g., timers and interrupt controllers), various I/O controllerperipherals, and routing logic.

The processor(s) 2110 may further include a safety cluster engine thatincludes a dedicated processor subsystem to handle safety management forautomotive applications. The safety cluster engine may include two ormore processor cores, a tightly coupled RAM, support peripherals (e.g.,timers, an interrupt controller, etc.), and/or routing logic. In asafety mode, the two or more cores may operate in a lockstep mode andfunction as a single core with comparison logic to detect anydifferences between their operations.

The processor(s) 2110 may further include a real-time camera engine thatmay include a dedicated processor subsystem for handling real-timecamera management.

The processor(s) 2110 may further include a high-dynamic range signalprocessor that may include an image signal processor that is a hardwareengine that is part of the camera processing pipeline.

The processor(s) 2110 may include a video image compositor that may be aprocessing block (e.g., implemented on a microprocessor) that implementsvideo post-processing functions needed by a video playback applicationto produce the final image for the player window. The video imagecompositor may perform lens distortion correction on wide-view camera(s)2170, surround camera(s) 2174, and/or on in-cabin monitoring camerasensors. In-cabin monitoring camera sensor is preferably monitored by aneural network running on another instance of the Advanced SoC,configured to identify in cabin events and respond accordingly. Anin-cabin system may perform lip reading to activate cellular service andplace a phone call, dictate emails, change the vehicle's destination,activate or change the vehicle's infotainment system and settings, orprovide voice-activated web surfing. Certain functions are available tothe driver only when the vehicle is operating in an autonomous mode, andare disabled otherwise.

The video image compositor may include enhanced temporal noise reductionfor both spatial and temporal noise reduction. For example, where motionoccurs in a video, the noise reduction weights spatial informationappropriately, decreasing the weight of information provided by adjacentframes. Where an image or portion of an image does not include motion,the temporal noise reduction performed by the video image compositor mayuse information from the previous image to reduce noise in the currentimage.

The video image compositor may also be configured to perform stereorectification on input stereo lens frames. The video image compositormay further be used for user interface composition when the operatingsystem desktop is in use, and the GPU(s) 2108 is not required tocontinuously render new surfaces. Even when the GPU(s) 2108 is poweredon and active doing 3D rendering, the video image compositor may be usedto offload the GPU(s) 2108 to improve performance and responsiveness.

The SoC(s) 2104 may further include a mobile industry processorinterface (MIPI) camera serial interface for receiving video and inputfrom cameras, a high-speed interface, and/or a video input block thatmay be used for camera and related pixel input functions. The SoC(s)2104 may further include an input/output controller(s) that may becontrolled by software and may be used for receiving I/O signals thatare uncommitted to a specific role.

The SoC(s) 2104 may further include a broad range of peripheralinterfaces to enable communication with peripherals, audio codecs, powermanagement, and/or other devices. The SoC(s) 2104 may be used to processdata from cameras (e.g., connected over Gigabit Multimedia Serial Linkand Ethernet), sensors (e.g., LIDAR sensor(s) 2164, RADAR sensor(s)2160, etc. that may be connected over Ethernet), data from bus 2102(e.g., speed of vehicle 2100, steering wheel position, etc.), data fromGNSS sensor(s) 2158 (e.g., connected over Ethernet or CAN bus). TheSoC(s) 2104 may further include dedicated high-performance mass storagecontrollers that may include their own DMA engines, and that may be usedto free the CPU(s) 2106 from routine data management tasks.

The SoC(s) 2104 may be an end-to-end platform with a flexiblearchitecture that spans automation levels 3-5, thereby providing acomprehensive functional safety architecture that leverages and makesefficient use of computer vision and ADAS techniques for diversity andredundancy, provides a platform for a flexible, reliable drivingsoftware stack, along with deep learning tools. The SoC(s) 2104 may befaster, more reliable, and even more energy-efficient andspace-efficient than conventional systems. For example, theaccelerator(s) 2114, when combined with the CPU(s) 2106, the GPU(s)2108, and the data store(s) 2116, may provide for a fast, efficientplatform for level 3-5 autonomous vehicles.

The technology thus provides capabilities and functionality that cannotbe achieved by conventional systems. For example, computer visionalgorithms may be executed on CPUs, which may be configured usinghigh-level programming language, such as the C programming language, toexecute a wide variety of processing algorithms across a wide variety ofvisual data. However, CPUs are oftentimes unable to meet the performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In particular,many CPUs are unable to execute complex object detection algorithms inreal-time, which is a requirement of in-vehicle ADAS applications, and arequirement for practical Level 3-5 autonomous vehicles.

In contrast to conventional systems, by providing a CPU complex, GPUcomplex, and a hardware acceleration cluster, the technology describedherein allows for multiple neural networks to be performedsimultaneously and/or sequentially, and for the results to be combinedtogether to enable Level 3-5 autonomous driving functionality. Forexample, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 2120) mayinclude a text and word recognition, allowing the supercomputer to readand understand traffic signs, including signs for which the neuralnetwork has not been specifically trained. The DLA may further include aneural network that is able to identify, interpret, and providessemantic understanding of the sign, and to pass that semanticunderstanding to the path planning modules running on the CPU Complex.

As another example, multiple neural networks may be run simultaneously,as is required for Level 3, 4, or 5 driving. For example, a warning signconsisting of “Caution: flashing lights indicate icy conditions,” alongwith an electric light, may be independently or collectively interpretedby several neural networks. The sign itself may be identified as atraffic sign by a first deployed neural network (e.g., a neural networkthat has been trained), the text “Flashing lights indicate icyconditions” may be interpreted by a second deployed neural network,which informs the vehicle's path planning software (preferably executingon the CPU Complex) that when flashing lights are detected, icyconditions exist. The flashing light may be identified by operating athird deployed neural network over multiple frames, informing thevehicle's path-planning software of the presence (or absence) offlashing lights. All three neural networks may run simultaneously, suchas within the DLA and/or on the GPU(s) 2108.

In some examples, a CNN for facial recognition and vehicle owneridentification may use data from camera sensors to identify the presenceof an authorized driver and/or owner of the vehicle 2100. The always onsensor processing engine may be used to unlock the vehicle when theowner approaches the driver door and turn on the lights, and, insecurity mode, to disable the vehicle when the owner leaves the vehicle.In this way, the SoC(s) 2104 provide for security against theft and/orcarjacking.

In another example, a CNN for emergency vehicle detection andidentification may use data from microphones 2196 to detect and identifyemergency vehicle sirens. In contrast to conventional systems, that usegeneral classifiers to detect sirens and manually extract features, theSoC(s) 2104 use the CNN for classifying environmental and urban sounds,as well as classifying visual data. In a preferred embodiment, the CNNrunning on the DLA is trained to identify the relative closing speed ofthe emergency vehicle (e.g., by using the Doppler Effect). The CNN mayalso be trained to identify emergency vehicles specific to the localarea in which the vehicle is operating, as identified by GNSS sensor(s)2158. Thus, for example, when operating in Europe the CNN will seek todetect European sirens, and when in the United States the CNN will seekto identify only North American sirens. Once an emergency vehicle isdetected, a control program may be used to execute an emergency vehiclesafety routine, slowing the vehicle, pulling over to the side of theroad, parking the vehicle, and/or idling the vehicle, with theassistance of ultrasonic sensors 2162, until the emergency vehicle(s)passes.

The vehicle may include a CPU(s) 2118 (e.g., discrete CPU(s), ordCPU(s)), that may be coupled to the SoC(s) 2104 via a high-speedinterconnect (e.g., PCIe). The CPU(s) 2118 may include an X86 processor,for example. The CPU(s) 2118 may be used to perform any of a variety offunctions, including arbitrating potentially inconsistent resultsbetween ADAS sensors and the SoC(s) 2104, and/or monitoring the statusand health of the controller(s) 2136 and/or infotainment SoC 2130, forexample.

The vehicle 2100 may include a GPU(s) 2120 (e.g., discrete GPU(s), ordGPU(s)), that may be coupled to the SoC(s) 2104 via a high-speedinterconnect (e.g., NVIDIA's NVLINK). The GPU(s) 2120 may provideadditional artificial intelligence functionality, such as by executingredundant and/or different neural networks, and may be used to trainand/or update neural networks based on input (e.g., sensor data) fromsensors of the vehicle 2100.

The vehicle 2100 may further include the network interface 2124 whichmay include one or more wireless antennas 2126 (e.g., one or morewireless antennas for different communication protocols, such as acellular antenna, a Bluetooth antenna, etc.). The network interface 2124may be used to enable wireless connectivity over the Internet with thecloud (e.g., with the server(s) 2178 and/or other network devices), withother vehicles, and/or with computing devices (e.g., client devices ofpassengers). To communicate with other vehicles, a direct link may beestablished between the two vehicles and/or an indirect link may beestablished (e.g., across networks and over the Internet). Direct linksmay be provided using a vehicle-to-vehicle communication link. Thevehicle-to-vehicle communication link may provide the vehicle 2100information about vehicles in proximity to the vehicle 2100 (e.g.,vehicles in front of, on the side of, and/or behind the vehicle 2100).This functionality may be part of a cooperative adaptive cruise controlfunctionality of the vehicle 2100.

The network interface 2124 may include a SoC that provides modulationand demodulation functionality and enables the controller(s) 2136 tocommunicate over wireless networks. The network interface 2124 mayinclude a radio frequency front-end for up-conversion from baseband toradio frequency, and down conversion from radio frequency to baseband.The frequency conversions may be performed through well-known processes,and/or may be performed using super-heterodyne processes. In someexamples, the radio frequency front end functionality may be provided bya separate chip. The network interface may include wirelessfunctionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000,Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or otherwireless protocols.

The vehicle 2100 may further include data store(s) 2128 which mayinclude off-chip (e.g., off the SoC(s) 2104) storage. The data store(s)2128 may include one or more storage elements including RAM, SRAM, DRAM,VRAM, Flash, hard disks, and/or other components and/or devices that maystore at least one bit of data.

The vehicle 2100 may further include GNSS sensor(s) 2158. The GNSSsensor(s) 2158 (e.g., GPS and/or assisted GPS sensors), to assist inmapping, perception, occupancy grid generation, and/or path planningfunctions. Any number of GNSS sensor(s) 2158 may be used, including, forexample and without limitation, a GPS using a USB connector with anEthernet to Serial (RS-232) bridge.

The vehicle 2100 may further include RADAR sensor(s) 2160. The RADARsensor(s) 2160 may be used by the vehicle 2100 for long-range vehicledetection, even in darkness and/or severe weather conditions. RADARfunctional safety levels may be ASIL B. The RADAR sensor(s) 2160 may usethe CAN and/or the bus 2102 (e.g., to transmit data generated by theRADAR sensor(s) 2160) for control and to access object tracking data,with access to Ethernet to access raw data in some examples. A widevariety of RADAR sensor types may be used. For example, and withoutlimitation, the RADAR sensor(s) 2160 may be suitable for front, rear,and side RADAR use. In some example, Pulse Doppler RADAR sensor(s) areused.

The RADAR sensor(s) 2160 may include different configurations, such aslong range with narrow field of view, short range with wide field ofview, short range side coverage, etc. In some examples, long-range RADARmay be used for adaptive cruise control functionality. The long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. The RADAR sensor(s)2160 may help in distinguishing between static and moving objects, andmay be used by ADAS systems for emergency brake assist and forwardcollision warning. Long-range RADAR sensors may include monostaticmultimodal RADAR with multiple (e.g., six or more) fixed RADAR antennaeand a high-speed CAN and FlexRay interface. In an example with sixantennae, the central four antennae may create a focused beam pattern,designed to record the vehicle's 2100 surroundings at higher speeds withminimal interference from traffic in adjacent lanes. The other twoantennae may expand the field of view, making it possible to quicklydetect vehicles entering or leaving the vehicle's 2100 lane.

Mid-range RADAR systems may include, as an example, a range of up to2160 m (front) or 80 m (rear), and a field of view of up to 42 degrees(front) or 2150 degrees (rear). Short-range RADAR systems may include,without limitation, RADAR sensors designed to be installed at both endsof the rear bumper. When installed at both ends of the rear bumper, sucha RADAR sensor systems may create two beams that constantly monitor theblind spot in the rear and next to the vehicle.

Short-range RADAR systems may be used in an ADAS system for blind spotdetection and/or lane change assist.

The vehicle 2100 may further include ultrasonic sensor(s) 2162. Theultrasonic sensor(s) 2162, which may be positioned at the front, back,and/or the sides of the vehicle 2100, may be used for park assist and/orto create and update an occupancy grid. A wide variety of ultrasonicsensor(s) 2162 may be used, and different ultrasonic sensor(s) 2162 maybe used for different ranges of detection (e.g., 2.5 m, 4 m). Theultrasonic sensor(s) 2162 may operate at functional safety levels ofASIL B.

The vehicle 2100 may include LIDAR sensor(s) 2164. The LIDAR sensor(s)2164 may be used for object and pedestrian detection, emergency braking,collision avoidance, and/or other functions. The LIDAR sensor(s) 2164may be functional safety level ASIL B. In some examples, the vehicle2100 may include multiple LIDAR sensors 2164 (e.g., two, four, six,etc.) that may use Ethernet (e.g., to provide data to a Gigabit Ethernetswitch).

In some examples, the LIDAR sensor(s) 2164 may be capable of providing alist of objects and their distances for a 360-degree field of view.Commercially available LIDAR sensor(s) 2164 may have an advertised rangeof approximately 2100 m, with an accuracy of 2 cm-3 cm, and with supportfor a 2100 Mbps Ethernet connection, for example. In some examples, oneor more non-protruding LIDAR sensors 2164 may be used. In such examples,the LIDAR sensor(s) 2164 may be implemented as a small device that maybe embedded into the front, rear, sides, and/or corners of the vehicle2100. The LIDAR sensor(s) 2164, in such examples, may provide up to a2120-degree horizontal and 35-degree vertical field-of-view, with a 200m range even for low-reflectivity objects. Front-mounted LIDAR sensor(s)2164 may be configured for a horizontal field of view between 45 degreesand 135 degrees.

In some examples, LIDAR technologies, such as 3D flash LIDAR, may alsobe used. 3D Flash LIDAR uses a flash of a laser as a transmissionsource, to illuminate vehicle surroundings up to approximately 200 m. Aflash LIDAR unit includes a receptor, which records the laser pulsetransit time and the reflected light on each pixel, which in turncorresponds to the range from the vehicle to the objects. Flash LIDARmay allow for highly accurate and distortion-free images of thesurroundings to be generated with every laser flash. In some examples,four flash LIDAR sensors may be deployed, one at each side of thevehicle 2100. Available 3D flash LIDAR systems include a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). The flash LIDAR device may use a 5nanosecond class I (eye-safe) laser pulse per frame and may capture thereflected laser light in the form of 3D range point clouds andco-registered intensity data. By using flash LIDAR, and because flashLIDAR is a solid-state device with no moving parts, the LIDAR sensor(s)2164 may be less susceptible to motion blur, vibration, and/or shock.

The vehicle may further include IMU sensor(s) 2166. The IMU sensor(s)2166 may be located at a center of the rear axle of the vehicle 2100, insome examples. The IMU sensor(s) 2166 may include, for example andwithout limitation, an accelerometer(s), a magnetometer(s), agyroscope(s), a magnetic compass(es), and/or other sensor types. In someexamples, such as in six-axis applications, the IMU sensor(s) 2166 mayinclude accelerometers and gyroscopes, while in nine-axis applications,the IMU sensor(s) 2166 may include accelerometers, gyroscopes, andmagnetometers.

In some embodiments, the IMU sensor(s) 2166 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertialsensors, a high-sensitivity GPS receiver, and advanced Kalman filteringalgorithms to provide estimates of position, velocity, and attitude. Assuch, in some examples, the IMU sensor(s) 2166 may enable the vehicle2100 to estimate heading without requiring input from a magnetic sensorby directly observing and correlating the changes in velocity from GPSto the IMU sensor(s) 2166. In some examples, the IMU sensor(s) 2166 andthe GNSS sensor(s) 2158 may be combined in a single integrated unit.

The vehicle may include microphone(s) 2196 placed in and/or around thevehicle 2100. The microphone(s) 2196 may be used for emergency vehicledetection and identification, among other things.

The vehicle may further include any number of camera types, includingstereo camera(s) 2168, wide-view camera(s) 2170, infrared camera(s)2172, surround camera(s) 2174, long-range and/or mid-range camera(s)2198, and/or other camera types. The cameras may be used to captureimage data around an entire periphery of the vehicle 2100. The types ofcameras used depends on the embodiments and requirements for the vehicle2100, and any combination of camera types may be used to provide thenecessary coverage around the vehicle 2100. In addition, the number ofcameras may differ depending on the embodiment. For example, the vehiclemay include six cameras, seven cameras, ten cameras, twelve cameras,and/or another number of cameras. The cameras may support, as an exampleand without limitation, Gigabit Multimedia Serial Link (GMSL) and/orGigabit Ethernet. Each of the camera(s) is described with more detailherein with respect to FIG. 21A and FIG. 21B.

The vehicle 2100 may further include vibration sensor(s) 2142. Thevibration sensor(s) 2142 may measure vibrations of components of thevehicle, such as the axle(s). For example, changes in vibrations mayindicate a change in road surfaces. In another example, when two or morevibration sensors 2142 are used, the differences between the vibrationsmay be used to determine friction or slippage of the road surface (e.g.,when the difference in vibration is between a power-driven axle and afreely rotating axle).

The vehicle 2100 may include an ADAS system 2138. The ADAS system 2138may include a SoC, in some examples. The ADAS system 2138 may includeautonomous/adaptive/automatic cruise control (ACC), cooperative adaptivecruise control (CACC), forward crash warning (FCW), automatic emergencybraking (AEB), lane departure warnings (LDW), lane keep assist (LKA),blind spot warning (BSW), rear cross-traffic warning (RCTW), collisionwarning systems (CWS), lane centering (LC), and/or other features andfunctionality.

The ACC systems may use RADAR sensor(s) 2160, LIDAR sensor(s) 2164,and/or a camera(s). The ACC systems may include longitudinal ACC and/orlateral ACC. Longitudinal ACC monitors and controls the distance to thevehicle immediately ahead of the vehicle 2100 and automatically adjustthe vehicle speed to maintain a safe distance from vehicles ahead.Lateral ACC performs distance keeping, and advises the vehicle 2100 tochange lanes when necessary. Lateral ACC is related to other ADASapplications such as LCA and CWS.

CACC uses information from other vehicles that may be received via thenetwork interface 2124 and/or the wireless antenna(s) 2126 from othervehicles via a wireless link, or indirectly, over a network connection(e.g., over the Internet). Direct links may be provided by avehicle-to-vehicle (V2V) communication link, while indirect links may beinfrastructure-to-vehicle (I2V) communication link. In general, the V2Vcommunication concept provides information about the immediatelypreceding vehicles (e.g., vehicles immediately ahead of and in the samelane as the vehicle 2100), while the I2V communication concept providesinformation about traffic further ahead. CACC systems may include eitheror both I2V and V2V information sources. Given the information of thevehicles ahead of the vehicle 2100, CACC may be more reliable and it haspotential to improve traffic flow smoothness and reduce congestion onthe road.

FCW systems are designed to alert the driver to a hazard, so that thedriver may take corrective action. FCW systems use a front-facing cameraand/or RADAR sensor(s) 2160, coupled to a dedicated processor, DSP,FPGA, and/or ASIC, that is electrically coupled to driver feedback, suchas a display, speaker, and/or vibrating component. FCW systems mayprovide a warning, such as in the form of a sound, visual warning,vibration and/or a quick brake pulse.

AEB systems detect an impending forward collision with another vehicleor other object, and may automatically apply the brakes if the driverdoes not take corrective action within a specified time or distanceparameter. AEB systems may use front-facing camera(s) and/or RADARsensor(s) 2160, coupled to a dedicated processor, DSP, FPGA, and/orASIC. When the AEB system detects a hazard, it typically first alertsthe driver to take corrective action to avoid the collision and, if thedriver does not take corrective action, the AEB system may automaticallyapply the brakes in an effort to prevent, or at least mitigate, theimpact of the predicted collision. AEB systems, may include techniquessuch as dynamic brake support and/or crash imminent braking.

LDW systems provide visual, audible, and/or tactile warnings, such assteering wheel or seat vibrations, to alert the driver when the vehicle2100 crosses lane markings. A LDW system does not activate when thedriver indicates an intentional lane departure, by activating a turnsignal. LDW systems may use front-side facing cameras, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

LKA systems are a variation of LDW systems. LKA systems provide steeringinput or braking to correct the vehicle 2100 if the vehicle 2100 startsto exit the lane.

BSW systems detects and warn the driver of vehicles in an automobile'sblind spot. BSW systems may provide a visual, audible, and/or tactilealert to indicate that merging or changing lanes is unsafe. The systemmay provide an additional warning when the driver uses a turn signal.BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s)2160, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component.

RCTW systems may provide visual, audible, and/or tactile notificationwhen an object is detected outside the rear-camera range when thevehicle 2100 is backing up. Some RCTW systems include AEB to ensure thatthe vehicle brakes are applied to avoid a crash. RCTW systems may useone or more rear-facing RADAR sensor(s) 2160, coupled to a dedicatedprocessor, DSP, FPGA, and/or ASIC, that is electrically coupled todriver feedback, such as a display, speaker, and/or vibrating component.

Conventional ADAS systems may be prone to false positive results whichmay be annoying and distracting to a driver, but typically are notcatastrophic, because the ADAS systems alert the driver and allow thedriver to decide whether a safety condition truly exists and actaccordingly. However, in an autonomous vehicle 2100, the vehicle 2100itself must, in the case of conflicting results, decide whether to heedthe result from a primary computer or a secondary computer (e.g., afirst controller 2136 or a second controller 2136). For example, in someembodiments, the ADAS system 2138 may be a backup and/or secondarycomputer for providing perception information to a backup computerrationality module. The backup computer rationality monitor may run aredundant diverse software on hardware components to detect faults inperception and dynamic driving tasks. Outputs from the ADAS system 2138may be provided to a supervisory MCU. If outputs from the primarycomputer and the secondary computer conflict, the supervisory MCU mustdetermine how to reconcile the conflict to ensure safe operation.

In some examples, the primary computer may be configured to provide thesupervisory MCU with a confidence score, indicating the primarycomputer's confidence in the chosen result. If the confidence scoreexceeds a threshold, the supervisory MCU may follow the primarycomputer's direction, regardless of whether the secondary computerprovides a conflicting or inconsistent result. Where the confidencescore does not meet the threshold, and where the primary and secondarycomputer indicate different results (e.g., the conflict), thesupervisory MCU may arbitrate between the computers to determine theappropriate outcome.

The supervisory MCU may be configured to run a neural network(s) that istrained and configured to determine, based on outputs from the primarycomputer and the secondary computer, conditions under which thesecondary computer provides false alarms. Thus, the neural network(s) inthe supervisory MCU may learn when the secondary computer's output maybe trusted, and when it cannot. For example, when the secondary computeris a RADAR-based FCW system, a neural network(s) in the supervisory MCUmay learn when the FCW system is identifying metallic objects that arenot, in fact, hazards, such as a drainage grate or manhole cover thattriggers an alarm. Similarly, when the secondary computer is acamera-based LDW system, a neural network in the supervisory MCU maylearn to override the LDW when bicyclists or pedestrians are present anda lane departure is, in fact, the safest maneuver. In embodiments thatinclude a neural network(s) running on the supervisory MCU, thesupervisory MCU may include at least one of a DLA or GPU suitable forrunning the neural network(s) with associated memory. In preferredembodiments, the supervisory MCU may comprise and/or be included as acomponent of the SoC(s) 2104.

In other examples, ADAS system 2138 may include a secondary computerthat performs ADAS functionality using traditional rules of computervision. As such, the secondary computer may use classic computer visionrules (if-then), and the presence of a neural network(s) in thesupervisory MCU may improve reliability, safety and performance. Forexample, the diverse implementation and intentional non-identity makesthe overall system more fault-tolerant, especially to faults caused bysoftware (or software-hardware interface) functionality. For example, ifthere is a software bug or error in the software running on the primarycomputer, and the non-identical software code running on the secondarycomputer provides the same overall result, the supervisory MCU may havegreater confidence that the overall result is correct, and the bug insoftware or hardware on primary computer is not causing material error.

In some examples, the output of the ADAS system 2138 may be fed into theprimary computer's perception block and/or the primary computer'sdynamic driving task block. For example, if the ADAS system 2138indicates a forward crash warning due to an object immediately ahead,the perception block may use this information when identifying objects.In other examples, the secondary computer may have its own neuralnetwork which is trained and thus reduces the risk of false positives,as described herein.

The vehicle 2100 may further include the infotainment SoC 2130 (e.g., anin-vehicle infotainment system (IVI)). Although illustrated anddescribed as a SoC, the infotainment system may not be a SoC, and mayinclude two or more discrete components. The infotainment SoC 2130 mayinclude a combination of hardware and software that may be used toprovide audio (e.g., music, a personal digital assistant, navigationalinstructions, news, radio, etc.), video (e.g., TV, movies, streaming,etc.), phone (e.g., hands-free calling), network connectivity (e.g.,LTE, Wi-Fi, etc.), and/or information services (e.g., navigationsystems, rear-parking assistance, a radio data system, vehicle relatedinformation such as fuel level, total distance covered, brake fuellevel, oil level, door open/close, air filter information, etc.) to thevehicle 2100. For example, the infotainment SoC 2130 may radios, diskplayers, navigation systems, video players, USB and Bluetoothconnectivity, carputers, in-car entertainment, Wi-Fi, steering wheelaudio controls, hands free voice control, a heads-up display (HUD), anHMI display 2134, a telematics device, a control panel (e.g., forcontrolling and/or interacting with various components, features, and/orsystems), and/or other components. The infotainment SoC 2130 may furtherbe used to provide information (e.g., visual and/or audible) to auser(s) of the vehicle, such as information from the ADAS system 2138,autonomous driving information such as planned vehicle maneuvers,trajectories, surrounding environment information (e.g., intersectioninformation, vehicle information, road information, etc.), and/or otherinformation.

The infotainment SoC 2130 may include GPU functionality. Theinfotainment SoC 2130 may communicate over the bus 2102 (e.g., CAN bus,Ethernet, etc.) with other devices, systems, and/or components of thevehicle 2100. In some examples, the infotainment SoC 2130 may be coupledto a supervisory MCU such that the GPU of the infotainment system mayperform some self-driving functions in the event that the primarycontroller(s) 2136 (e.g., the primary and/or backup computers of thevehicle 2100) fail. In such an example, the infotainment SoC 2130 mayput the vehicle 2100 into a chauffeur to safe stop mode, as describedherein.

The vehicle 2100 may further include an instrument cluster 2132 (e.g., adigital dash, an electronic instrument cluster, a digital instrumentpanel, etc.). The instrument cluster 2132 may include a controllerand/or supercomputer (e.g., a discrete controller or supercomputer). Theinstrument cluster 2132 may include a set of instrumentation such as aspeedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s), airbag(SRS) system information, lighting controls, safety system controls,navigation information, etc. In some examples, information may bedisplayed and/or shared among the infotainment SoC 2130 and theinstrument cluster 2132. In other words, the instrument cluster 2132 maybe included as part of the infotainment SoC 2130, or vice versa.

FIG. 21D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle 2100 of FIG. 21A, inaccordance with some embodiments of the present disclosure. The system2176 may include server(s) 2178, network(s) 2190, and vehicles,including the vehicle 2100. The server(s) 2178 may include a pluralityof GPUs 2184(A)-2184(H) (collectively referred to herein as GPUs 2184),PCIe switches 2182(A)-2182(H) (collectively referred to herein as PCIeswitches 2182), and/or CPUs 2180(A)-2180(B) (collectively referred toherein as CPUs 2180). The GPUs 2184, the CPUs 2180, and the PCIeswitches may be interconnected with high-speed interconnects such as,for example and without limitation, NVLink interfaces 2188 developed byNVIDIA and/or PCIe connections 2186. In some examples, the GPUs 2184 areconnected via NVLink and/or NVSwitch SoC and the GPUs 2184 and the PCIeswitches 2182 are connected via PCIe interconnects. Although eight GPUs2184, two CPUs 2180, and two PCIe switches are illustrated, this is notintended to be limiting. Depending on the embodiment, each of theserver(s) 2178 may include any number of GPUs 2184, CPUs 2180, and/orPCIe switches. For example, the server(s) 2178 may each include eight,sixteen, thirty-two, and/or more GPUs 2184.

The server(s) 2178 may receive, over the network(s) 2190 and from thevehicles, image data representative of images showing unexpected orchanged road conditions, such as recently commenced road-work. Theserver(s) 2178 may transmit, over the network(s) 2190 and to thevehicles, neural networks 2192, updated neural networks 2192, and/or mapinformation 2194, including information regarding traffic and roadconditions. The updates to the map information 2194 may include updatesfor the HD map 2122, such as information regarding construction sites,potholes, detours, flooding, and/or other obstructions. In someexamples, the neural networks 2192, the updated neural networks 2192,and/or the map information 2194 may have resulted from new trainingand/or experiences represented in data received from any number ofvehicles in the environment, and/or based on training performed at adatacenter (e.g., using the server(s) 2178 and/or other servers).

The server(s) 2178 may be used to train machine learning models (e.g.,neural networks) based on training data. The training data may begenerated by the vehicles, and/or may be generated in a simulation(e.g., using a game engine). In some examples, the training data istagged (e.g., where the neural network benefits from supervisedlearning) and/or undergoes other pre-processing, while in other examplesthe training data is not tagged and/or pre-processed (e.g., where theneural network does not require supervised learning). Once the machinelearning models are trained, the machine learning models may be used bythe vehicles (e.g., transmitted to the vehicles over the network(s)2190, and/or the machine learning models may be used by the server(s)2178 to remotely monitor the vehicles.

In some examples, the server(s) 2178 may receive data from the vehiclesand apply the data to up-to-date real-time neural networks for real-timeintelligent inferencing. The server(s) 2178 may include deep-learningsupercomputers and/or dedicated AI computers powered by GPU(s) 2184,such as a DGX and DGX Station machines developed by NVIDIA. However, insome examples, the server(s) 2178 may include deep learninginfrastructure that use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s) 2178 may be capable offast, real-time inferencing, and may use that capability to evaluate andverify the health of the processors, software, and/or associatedhardware in the vehicle 2100. For example, the deep-learninginfrastructure may receive periodic updates from the vehicle 2100, suchas a sequence of images and/or objects that the vehicle 2100 has locatedin that sequence of images (e.g., via computer vision and/or othermachine learning object classification techniques). The deep-learninginfrastructure may run its own neural network to identify the objectsand compare them with the objects identified by the vehicle 2100 and, ifthe results do not match and the infrastructure concludes that the AI inthe vehicle 2100 is malfunctioning, the server(s) 2178 may transmit asignal to the vehicle 2100 instructing a fail-safe computer of thevehicle 2100 to assume control, notify the passengers, and complete asafe parking maneuver.

For inferencing, the server(s) 2178 may include the GPU(s) 2184 and oneor more programmable inference accelerators (e.g., NVIDIA's TensorRT).The combination of GPU-powered servers and inference acceleration maymake real-time responsiveness possible. In other examples, such as whereperformance is less critical, servers powered by CPUs, FPGAs, and otherprocessors may be used for inferencing.

Example Computing Device

FIG. 22 is a block diagram of an example computing device 2200 suitablefor use in implementing some embodiments of the present disclosure.Computing device 2200 may include a bus 2202 that directly or indirectlycouples the following devices: memory 2204, one or more centralprocessing units (CPUs) 2206, one or more graphics processing units(GPUs) 2208, a communication interface 2210, input/output (I/O) ports2212, input/output components 2214, a power supply 2216, and one or morepresentation components 2218 (e.g., display(s)).

Although the various blocks of FIG. 22 are shown as connected via thebus 2202 with lines, this is not intended to be limiting and is forclarity only. For example, in some embodiments, a presentation component2218, such as a display device, may be considered an I/O component 2214(e.g., if the display is a touch screen). As another example, the CPUs2206 and/or GPUs 2208 may include memory (e.g., the memory 2204 may berepresentative of a storage device in addition to the memory of the GPUs2208, the CPUs 2206, and/or other components). In other words, thecomputing device of FIG. 22 is merely illustrative. Distinction is notmade between such categories as “workstation,” “server,” “laptop,”“desktop,” “tablet,” “client device,” “mobile device,” “hand-helddevice,” “game console,” “electronic control unit (ECU),” “virtualreality system,” and/or other device or system types, as all arecontemplated within the scope of the computing device of FIG. 22 . Insome embodiments, one or more components described herein with respectto FIG. 22 may be used by the vehicle 2100, described herein. Forexample, the CPUs 1506, the GPUs 1508, and/or other components may besimilar to or may perform functions of one or more components of thevehicle 2100, described herein.

The bus 2202 may represent one or more busses, such as an address bus, adata bus, a control bus, or a combination thereof. The bus 2202 mayinclude one or more bus types, such as an industry standard architecture(ISA) bus, an extended industry standard architecture (EISA) bus, avideo electronics standards association (VESA) bus, a peripheralcomponent interconnect (PCI) bus, a peripheral component interconnectexpress (PCIe) bus, and/or another type of bus.

The memory 2204 may include any of a variety of computer-readable media.The computer-readable media may be any available media that may beaccessed by the computing device 2200. The computer-readable media mayinclude both volatile and nonvolatile media, and removable andnon-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer-storage media andcommunication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the memory 2204 may store computer-readable instructions(e.g., that represent a program(s) and/or a program element(s), such asan operating system. Computer-storage media may include, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by computing device2200. As used herein, computer storage media does not comprise signalsper se.

The communication media may embody computer-readable instructions, datastructures, program modules, and/or other data types in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. The term “modulated data signal” mayrefer to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal. By wayof example, and not limitation, the communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, RF, infrared and other wireless media.Combinations of any of the above should also be included within thescope of computer-readable media.

The CPU(s) 2206 may be configured to execute the computer-readableinstructions to control one or more components of the computing device2200 to perform one or more of the methods and/or processes describedherein. The CPU(s) 2206 may each include one or more cores (e.g., one,two, four, eight, twenty-eight, seventy-two, etc.) that are capable ofhandling a multitude of software threads simultaneously. The CPU(s) 2206may include any type of processor, and may include different types ofprocessors depending on the type of computing device 2200 implemented(e.g., processors with fewer cores for mobile devices and processorswith more cores for servers). For example, depending on the type ofcomputing device 2200, the processor may be an ARM processor implementedusing Reduced Instruction Set Computing (RISC) or an x86 processorimplemented using Complex Instruction Set Computing (CISC). Thecomputing device 2200 may include one or more CPUs 2206 in addition toone or more microprocessors or supplementary co-processors, such as mathco-processors.

The GPU(s) 2208 may be used by the computing device 2200 to rendergraphics (e.g., 3D graphics). The GPU(s) 2208 may include hundreds orthousands of cores that are capable of handling hundreds or thousands ofsoftware threads simultaneously. The GPU(s) 2208 may generate pixel datafor output images in response to rendering commands (e.g., renderingcommands from the CPU(s) 2206 received via a host interface). The GPU(s)2208 may include graphics memory, such as display memory, for storingpixel data. The display memory may be included as part of the memory2204. The GPU(s) 708 may include two or more GPUs operating in parallel(e.g., via a link). When combined together, each GPU 2208 may generatepixel data for different portions of an output image or for differentoutput images (e.g., a first GPU for a first image and a second GPU fora second image). Each GPU may include its own memory, or may sharememory with other GPUs.

In examples where the computing device 2200 does not include the GPU(s)2208, the CPU(s) 2206 may be used to render graphics.

The communication interface 2210 may include one or more receivers,transmitters, and/or transceivers that enable the computing device 700to communicate with other computing devices via an electroniccommunication network, included wired and/or wireless communications.The communication interface 2210 may include components andfunctionality to enable communication over any of a number of differentnetworks, such as wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth,Bluetooth LE, ZigBee, etc.), wired networks (e.g., communicating overEthernet), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.),and/or the Internet.

The I/O ports 2212 may enable the computing device 2200 to be logicallycoupled to other devices including the I/O components 2214, thepresentation component(s) 2218, and/or other components, some of whichmay be built in to (e.g., integrated in) the computing device 2200.Illustrative I/O components 2214 include a microphone, mouse, keyboard,joystick, game pad, game controller, satellite dish, scanner, printer,wireless device, etc. The I/O components 2214 may provide a natural userinterface (NUI) that processes air gestures, voice, or otherphysiological inputs generated by a user. In some instances, inputs maybe transmitted to an appropriate network element for further processing.An NUI may implement any combination of speech recognition, stylusrecognition, facial recognition, biometric recognition, gesturerecognition both on screen and adjacent to the screen, air gestures,head and eye tracking, and touch recognition (as described in moredetail below) associated with a display of the computing device 2200.The computing device 2200 may be include depth cameras, such asstereoscopic camera systems, infrared camera systems, RGB camerasystems, touchscreen technology, and combinations of these, for gesturedetection and recognition. Additionally, the computing device 2200 mayinclude accelerometers or gyroscopes (e.g., as part of an inertiameasurement unit (IMU)) that enable detection of motion. In someexamples, the output of the accelerometers or gyroscopes may be used bythe computing device 2200 to render immersive augmented reality orvirtual reality.

The power supply 2216 may include a hard-wired power supply, a batterypower supply, or a combination thereof. The power supply 2216 mayprovide power to the computing device 2200 to enable the components ofthe computing device 2200 to operate.

The presentation component(s) 2218 may include a display (e.g., amonitor, a touch screen, a television screen, a heads-up-display (HUD),other display types, or a combination thereof), speakers, and/or otherpresentation components. The presentation component(s) 2218 may receivedata from other components (e.g., the GPU(s) 2208, the CPU(s) 2206,etc.), and output the data (e.g., as an image, video, sound, etc.).

The disclosure may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Thedisclosure may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The disclosure mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

As used herein, a recitation of “and/or” with respect to two or moreelements should be interpreted to mean only one element, or acombination of elements. For example, “element A, element B, and/orelement C” may include only element A, only element B, only element C,element A and element B, element A and element C, element B and elementC, or elements A, B, and C. In addition, “at least one of element A orelement B” may include at least one of element A, at least one ofelement B, or at least one of element A and at least one of element B.Further, “at least one of element A and element B” may include at leastone of element A, at least one of element B, or at least one of elementA and at least one of element B.

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of thisdisclosure. Rather, the inventors have contemplated that the claimedsubject matter might also be embodied in other ways, to includedifferent steps or combinations of steps similar to the ones describedin this document, in conjunction with other present or futuretechnologies. Moreover, although the terms “step” and/or “block” may beused herein to connote different elements of methods employed, the termsshould not be interpreted as implying any particular order among orbetween various steps herein disclosed unless and except when the orderof individual steps is explicitly described.

What is claimed is:
 1. A method comprising: determining, using one ormore machine learning models and based at least on sensor data obtainedusing one or more sensors of a machine, depth data corresponding to oneor more free-space locations associated with an environment; andperforming, based at least on the depth data, one or more operationsassociated with the machine.
 2. The method of claim 1, wherein the depthdata represents one or more locations of one or more boundariescorresponding to the one or more free-space locations associated withthe environment.
 3. The method of claim 1, wherein the depth datarepresents a depth map associated with the environment, the depth mapindicating the one or more free-space locations within the environment.4. The method of claim 1, wherein the sensor data includes image datarepresentative of at least an image, and wherein the depth datarepresents at least one or more points of the image that correspond tothe one or more free-space locations associated with the environment. 5.The method of claim 4, wherein the one or more points include one ormore two-dimensional (2D) points of the image, and wherein the methodfurther comprises: determining, based at least on the one or more 2Dpoints of the image, one or more three-dimensional (3D) pointscorresponding to the one or more free-space locations associated withthe environment, wherein the performing the one or more operationsassociated with the machine is based at least on the one or more 3Dpoints.
 6. The method of claim 1, wherein the determining the depth datacorresponding to the one or more free-space locations associated withthe environment comprises: determining, using the one or more machinelearning models and based at least on image data representative of animage depicting at least a portion the environment, one or more pointsof the image that correspond to the one or more free-space locations;and determining, using the one or more machine learning models and basedat least on the sensor data, the depth data representative of one ormore depth values corresponding to the one or more points.
 7. The methodof claim 1, further comprising: determining, using the one or moremachine learning models and based at least on the sensor data, seconddepth data corresponding to one or more objects located within theenvironment, wherein the performing the one or more operationsassociated with the machine is further based at least on the seconddepth data.
 8. The method of claim 1, further comprising: obtainingprofile data representative of a profile associated with at least aportion of a driving surface within the environment, wherein thedetermining the depth data corresponding to the one or more free-spacelocations associated with the environment is further based at least onthe profile data.
 9. The method of claim 1, wherein the one or moreoperations include at least one of path planning, world modelmanagement, obstacle or collision avoidance, a control decision, or anadvanced driver assistance system (ADAS) operation.
 10. A systemcomprising: one or more processing units to: determine, using one ormore machine learning models and based at least on sensor data obtainedusing one or more sensors of a machine, location informationcorresponding to one or more free-space locations associated with anenvironment; and perform, based at least on the location information,one or more operations associated with the machine.
 11. The system ofclaim 10, wherein the location information indicates one or morelocations of one or more boundaries corresponding to the one or morefree-space locations associated with the environment.
 12. The system ofclaim 10, wherein the location information is determined using a depthmap associated with the environment, the depth map indicating the one ormore free-space locations within the environment.
 13. The system ofclaim 10, wherein the sensor data includes image data representative ofat least an image, and wherein the location information indicates atleast one or more points associated with the image that correspond tothe one or more free-space locations associated with the environment.14. The system of claim 13, wherein the one or more points include oneor more two-dimensional (2D) points associated with the image, andwherein the one or more processing units are further to: determine,based at least on the one or more 2D points associated with the image,one or more three-dimensional (3D) points corresponding to the one ormore free-space locations associated with the environment, wherein theone or more operations associated with the machine are performed basedat least on the one or more 3D points.
 15. The system of claim 10,wherein the determination of the location information corresponding tothe one or more free-space locations associated with the environmentcomprises: determining, using the one or more machine learning modelsand based at least on image data representative of an image depicting atleast a portion of the environment, one or more points associated withthe image that correspond to the one or more free-space locations; anddetermining, using the one or more machine learning models and based atleast on the sensor data, the location information indicating the one ormore depth values corresponding to the one or more points.
 16. Thesystem of claim 10, wherein the one or more processing units are furtherto: determine, using the one or more machine learning models and basedat least on the sensor data, second location information correspondingto one or more objects located within the environment, wherein the oneor more operations associated with the machine are further performedbased at least on the second location information.
 17. The system ofclaim 10, wherein the one or more processing units are further to:obtain profile data representative of a profile associated with at leasta portion of a driving surface within the environment, wherein thedetermination of the location information corresponding to the one ormore free-space locations associated with the environment is furtherbased at least on the profile data.
 18. The system of claim 10, whereinthe system is comprised in at least one of: a control system for anautonomous or semi-autonomous machine; a perception system for anautonomous or semi-autonomous machine; a system for performingsimulation operations; a system for performing deep learning operations;a system implemented using an edge device; a system implemented using arobot; a system implemented at least partially in a data center; or asystem implemented at least partially using cloud computing resources.19. A processor comprising: one or more processing units to causeperformance of one or more control operations of a machine based atleast on one or more free-space boundaries within an environment,wherein the one or more free-space boundaries are determined using oneor more machine learning models and based at least one sensor dataobtained using one or more sensors of the machine.
 20. The processor ofclaim 19, wherein the processor is comprised in at least one of: acontrol system for an autonomous or semi-autonomous machine; aperception system for an autonomous or semi-autonomous machine; a systemfor performing simulation operations; a system for performing deeplearning operations; a system implemented using an edge device; a systemimplemented using a robot; a system implemented at least partially in adata center; or a system implemented at least partially using cloudcomputing resources.